Detecting anomalous behavior of a device

ABSTRACT

Detecting anomalous behavior of a device, including: generating, using information describing historical activity associated with a user device, a trained model for detecting normal activity for the user device; gathering information describing current activity associated with the user device; and determining, by using the information describing current activity associated with the user device as input to the trained model, whether the user device has deviated from normal activity.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate various embodiments and are a partof the specification. The illustrated embodiments are merely examplesand do not limit the scope of the disclosure. Throughout the drawings,identical or similar reference numbers designate identical or similarelements.

FIG. 1A shows an illustrative configuration in which a data platform isconfigured to perform various operations with respect to a cloudenvironment that includes a plurality of compute assets.

FIG. 1B shows an illustrative implementation of the configuration ofFIG. 1A.

FIG. 1C illustrates an example computing device.

FIG. 1D illustrates an example of an environment in which activitiesthat occur within datacenters are modeled.

FIG. 2A illustrates an example of a process, used by an agent, tocollect and report information about a client.

FIG. 2B illustrates a 5-tuple of data collected by an agent, physicallyand logically.

FIG. 2C illustrates a portion of a polygraph.

FIG. 2D illustrates a portion of a polygraph.

FIG. 2E illustrates an example of a communication polygraph.

FIG. 2F illustrates an example of a polygraph.

FIG. 2G illustrates an example of a polygraph as rendered in aninterface.

FIG. 2H illustrates an example of a portion of a polygraph as renderedin an interface.

FIG. 2I illustrates an example of a portion of a polygraph as renderedin an interface.

FIG. 2J illustrates an example of a portion of a polygraph as renderedin an interface.

FIG. 2K illustrates an example of a portion of a polygraph as renderedin an interface.

FIG. 2L illustrates an example of an insider behavior graph as renderedin an interface.

FIG. 2M illustrates an example of a privilege change graph as renderedin an interface.

FIG. 2N illustrates an example of a user login graph as rendered in aninterface.

FIG. 2O illustrates an example of a machine server graph as rendered inan interface.

FIG. 3A illustrates an example of a process for detecting anomalies in anetwork environment.

FIG. 3B depicts a set of example processes communicating with otherprocesses.

FIG. 3C depicts a set of example processes communicating with otherprocesses.

FIG. 3D depicts a set of example processes communicating with otherprocesses.

FIG. 3E depicts two pairs of clusters.

FIG. 3F is a representation of a user logging into a first machine, theninto a second machine from the first machine, and then making anexternal connection.

FIG. 3G is an alternate representation of actions occurring in FIG. 3F.

FIG. 3H illustrates an example of a process for performing extended usertracking.

FIG. 3I is a representation of a user logging into a first machine, theninto a second machine from the first machine, and then making anexternal connection.

FIG. 3J illustrates an example of a process for performing extended usertracking.

FIG. 3K illustrates example records.

FIG. 3L illustrates example output from performing an ssh connectionmatch.

FIG. 3M illustrates example records.

FIG. 3N illustrates example records.

FIG. 3O illustrates example records.

FIG. 3P illustrates example records.

FIG. 3Q illustrates an adjacency relationship between two loginsessions.

FIG. 3R illustrates example records.

FIG. 3S illustrates an example of a process for detecting anomalies.

FIG. 4A illustrates a representation of an embodiment of an insiderbehavior graph.

FIG. 4B illustrates an embodiment of a portion of an insider behaviorgraph.

FIG. 4C illustrates an embodiment of a portion of an insider behaviorgraph.

FIG. 4D illustrates an embodiment of a portion of an insider behaviorgraph.

FIG. 4E illustrates a representation of an embodiment of a user logingraph.

FIG. 4F illustrates an example of a privilege change graph.

FIG. 4G illustrates an example of a privilege change graph.

FIG. 4H illustrates an example of a user interacting with a portion ofan interface.

FIG. 4I illustrates an example of a dossier for an event.

FIG. 4J illustrates an example of a dossier for a domain.

FIG. 4K depicts an example of an Entity Join graph by FilterKey andFilterKey Group (implicit join).

FIG. 4L illustrates an example of a process for dynamically generatingand executing a query.

FIG. 5A sets forth a system for providing many of the features describedherein for user devices as a distributed edge service in accordance withsome embodiments of the present disclosure.

FIG. 5B sets forth a system for providing many of the features describedherein for user devices as a distributed edge service in accordance withsome embodiments of the present disclosure.

FIG. 6 sets forth a flow chart illustrating an example method ofdetecting deviations from typical user behavior in accordance with someembodiments of the present disclosure.

FIG. 7 sets forth a flow chart illustrating an additional example methodof detecting deviations from typical behavior in accordance with someembodiments of the present disclosure.

FIG. 8 sets forth a flow chart illustrating an additional example methodof detecting deviations from typical behavior in accordance with someembodiments of the present disclosure.

FIG. 9 sets forth a flow chart illustrating an example method ofdetecting a location of a user device in accordance with someembodiments of the present disclosure.

FIG. 10 sets forth a flow chart illustrating an additional examplemethod of detecting a location of a user device in accordance with someembodiments of the present disclosure.

FIG. 11 sets forth a flow chart illustrating an additional examplemethod of detecting a location of a user device in accordance with someembodiments of the present disclosure.

FIG. 12 sets forth a flow chart illustrating an example method ofdetecting deviation from normal behavior of a device in accordance withsome embodiments of the present disclosure.

FIG. 13 sets forth a flow chart illustrating an additional examplemethod of detecting deviation from normal behavior of a device accordingto embodiments of the present disclosure.

FIG. 14 sets forth a flow chart illustrating an additional examplemethod of detecting deviation from normal behavior of a device in someembodiments of the present disclosure.

FIG. 15A sets forth an example of a user-specific polygraph inaccordance with some embodiments of the present disclosure.

FIG. 15B sets forth an example of a user-specific polygraph inaccordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION

Various illustrative embodiments are described herein with reference tothe accompanying drawings. It will, however, be evident that variousmodifications and changes may be made thereto, and additionalembodiments may be implemented, without departing from the scope of theinvention as set forth in the claims. For example, certain features ofone embodiment described herein may be combined with or substituted forfeatures of another embodiment described herein. The description anddrawings are accordingly to be regarded in an illustrative rather than arestrictive sense.

FIG. 1A shows an illustrative configuration 10 in which a data platform12 is configured to perform various operations with respect to a cloudenvironment 14 that includes a plurality of compute assets 16-1 through16-N (collectively “compute assets 16). For example, data platform 12may include data ingestion resources 18 configured to ingest data fromcloud environment 14 into data platform 12, data processing resources 20configured to perform data processing operations with respect to thedata, user interface resources 22 configured to provide one or moreexternal users and/or compute resources (e.g., computing device 24) withaccess to an output of data processing resources 20. Each of theseresources are described in detail herein.

Cloud environment 14 may include any suitable network-based computingenvironment as may serve a particular application. For example, cloudenvironment 14 may be implemented by one or more compute resourcesprovided and/or otherwise managed by one or more cloud serviceproviders, such as Amazon Web Services (AWS), Google Cloud Platform(GCP), Microsoft Azure, and/or any other cloud service providerconfigured to provide public and/or private access to network-basedcompute resources.

Compute assets 16 may include, but are not limited to, containers (e.g.,container images, deployed and executing container instances, etc.),virtual machines, workloads, applications, processes, physical machines,compute nodes, clusters of compute nodes, software runtime environments(e.g., container runtime environments), and/or any other virtual and/orphysical compute resource that may reside in and/or be executed by oneor more computer resources in cloud environment 14. In some examples,one or more compute assets 16 may reside in one or more datacenters.

A compute asset 16 may be associated with (e.g., owned, deployed, ormanaged by) a particular entity, such as a customer or client of cloudenvironment 14 and/or data platform 12. Accordingly, for purposes of thediscussion herein, cloud environment 14 may be used by one or moreentities.

Data platform 12 may be configured to perform one or more data securitymonitoring and/or remediation services, compliance monitoring services,anomaly detection services, DevOps services, compute asset managementservices, and/or any other type of data analytics service as may serve aparticular implementation. Data platform 12 may be managed or otherwiseassociated with any suitable data platform provider, such as a providerof any of the data analytics services described herein. The variousresources included in data platform 12 may reside in the cloud and/or belocated on-premises and be implemented by any suitable combination ofphysical and/or virtual compute resources, such as one or more computingdevices, microservices, applications, etc.

Data ingestion resources 18 may be configured to ingest data from cloudenvironment 14 into data platform 12. This may be performed in variousways, some of which are described in detail herein. For example, asillustrated by arrow 26, data ingestion resources 18 may be configuredto receive the data from one or more agents deployed within cloudenvironment 14, utilize an event streaming platform (e.g., Kafka) toobtain the data, and/or pull data (e.g., configuration data) from cloudenvironment 14. In some examples, data ingestion resources 18 may obtainthe data using one or more agentless configurations.

The data ingested by data ingestion resources 18 from cloud environment14 may include any type of data as may serve a particularimplementation. For example, the data may include data representative ofconfiguration information associated with compute assets 16, informationabout one or more processes running on compute assets 16, networkactivity information, information about events (creation events,modification events, communication events, user-initiated events, etc.)that occur with respect to compute assets 16, etc. In some examples, thedata may or may not include actual customer data processed or otherwisegenerated by compute assets 16.

As illustrated by arrow 28, data ingestion resources 18 may beconfigured to load the data ingested from cloud environment 14 into adata store 30. Data store 30 is illustrated in FIG. 1A as being separatefrom and communicatively coupled to data platform 12. However, in somealternative embodiments, data store 30 is included within data platform12.

Data store 30 may be implemented by any suitable data warehouse, datalake, data mart, and/or other type of database structure as may serve aparticular implementation. Such data stores may be proprietary or may beembodied as vendor provided products or services such as, for example,Snowflake, Google BigQuery, Druid, Amazon Redshift, IBM db2, Dremio,Databricks Lakehouse Platform, Cloudera, Azure Synapse Analytics, andothers.

Although the examples described herein largely relate to embodimentswhere data is collected from agents and ultimately stored in a datastore such as those provided by Snowflake, in other embodiments datathat is collected from agents and other sources may be stored indifferent ways. For example, data that is collected from agents andother sources may be stored in a data warehouse, data lake, data mart,and/or any other data store.

A data warehouse may be embodied as an analytic database (e.g., arelational database) that is created from two or more data sources. Sucha data warehouse may be leveraged to store historical data, often on thescale of petabytes. Data warehouses may have compute and memoryresources for running complicated queries and generating reports. Datawarehouses may be the data sources for business intelligence (‘BI’)systems, machine learning applications, and/or other applications. Byleveraging a data warehouse, data that has been copied into the datawarehouse may be indexed for good analytic query performance, withoutaffecting the write performance of a database (e.g., an OnlineTransaction Processing (‘OLTP’) database). Data warehouses also enablethe joining data from multiple sources for analysis. For example, asales OLTP application probably has no need to know about the weather atvarious sales locations, but sales predictions could take advantage ofthat data. By adding historical weather data to a data warehouse, itwould be possible to factor it into models of historical sales data.

Data lakes, which store files of data in their native format, may beconsidered as “schema on read” resources. As such, any application thatreads data from the lake may impose its own types and relationships onthe data. Data warehouses, on the other hand, are “schema on write,”meaning that data types, indexes, and relationships are imposed on thedata as it is stored in an enterprise data warehouse (EDW). “Schema onread” resources may be beneficial for data that may be used in severalcontexts and poses little risk of losing data. “Schema on write”resources may be beneficial for data that has a specific purpose, andgood for data that must relate properly to data from other sources. Suchdata stores may include data that is encrypted using homomorphicencryption, data encrypted using privacy-preserving encryption, smartcontracts, non-fungible tokens, decentralized finance, and othertechniques.

Data marts may contain data oriented towards a specific business linewhereas data warehouses contain enterprise-wide data. Data marts may bedependent on a data warehouse, independent of the data warehouse (e.g.,drawn from an operational database or external source), or a hybrid ofthe two. In embodiments described herein, different types of data stores(including combinations thereof) may be leveraged.

Data processing resources 20 may be configured to perform various dataprocessing operations with respect to data ingested by data ingestionresources 18, including data ingested and stored in data store 30. Forexample, data processing resources 20 may be configured to perform oneor more data security monitoring and/or remediation operations,compliance monitoring operations, anomaly detection operations, DevOpsoperations, compute asset management operations, and/or any other typeof data analytics operation as may serve a particular implementation.Various examples of operations performed by data processing resources 20are described herein.

As illustrated by arrow 32, data processing resources 20 may beconfigured to access data in data store 30 to perform the variousoperations described herein. In some examples, this may includeperforming one or more queries with respect to the data stored in datastore 30. Such queries may be generated using any suitable querylanguage.

In some examples, the queries provided by data processing resources 20may be configured to direct data store 30 to perform one or more dataanalytics operations with respect to the data stored within data store30. These data analytics operations may be with respect to data specificto a particular entity (e.g., data residing in one or more silos withindata store 30 that are associated with a particular customer) and/ordata associated with multiple entities. For example, data processingresources 20 may be configured to analyze data associated with a firstentity and use the results of the analysis to perform one or moreoperations with respect to a second entity.

One or more operations performed by data processing resources 20 may beperformed periodically according to a predetermined schedule. Forexample, one or more operations may be performed by processing resources20 every hour or any other suitable time interval. Additionally oralternatively, one or more operations performed by data processingresources 20 may be performed in substantially real-time (or nearreal-time) as data is ingested into data platform 12. In this manner,the results of such operations (e.g., one or more detected anomalies inthe data) may be provided to one or more external entities (e.g.,computing device 24 and/or one or more users) in substantially real-timeand/or in near real-time.

User interface resources 22 may be configured to perform one or moreuser interface operations, examples of which are described herein. Forexample, user interface resources 22 may be configured to present one ormore results of the data processing performed by data processingresources 20 to one or more external entities (e.g., computing device 24and/or one or more users), as illustrated by arrow 34. As illustrated byarrow 36, user interface resources 22 may access data in data store 30to perform the one or more user interface operations

FIG. 1B illustrates an implementation of configuration 10 in which anagent 38 (e.g., agent 38-1 through agent 38-N) is installed on each ofcompute assets 16. As used herein, an agent may include a self-containedbinary and/or other type of code or application that can be run on anyappropriate platforms, including within containers and/or other virtualcompute assets. Agents 38 may monitor the nodes on which they executefor a variety of different activities, including but not limited to,connection, process, user, machine, and file activities. In someexamples, agents 38 can be executed in user space, and can use a varietyof kernel modules (e.g., auditd, iptables, netfilter, pcap, etc.) tocollect data. Agents can be implemented in any appropriate programminglanguage, such as C or Golang, using applicable kernel APIs.

Agents 38 may be deployed in any suitable manner. For example, an agent38 may be deployed as a containerized application or as part of acontainerized application. As described herein, agents 38 mayselectively report information to data platform 12 in varying amounts ofdetail and/or with variable frequency.

Also shown in FIG. 1B is a load balancer 40 configured to perform one ormore load balancing operations with respect to data ingestion operationsperformed by data ingestion resources 18 and/or user interfaceoperations performed by user interface resources 22. Load balancer 40 isshown to be included in data platform 12. However, load balancer 40 mayalternatively be located external to data platform 12. Load balancer 40may be implemented by any suitable microservice, application, and/orother computing resources. In some alternative examples, data platform12 may not utilize a load balancer such as load balancer 40.

Also shown in FIG. 1B is long term storage 42 with which data ingestionresources may interface, as illustrated by arrow 44. Long term storage42 may be implemented by any suitable type of storage resources, such ascloud-based storage (e.g., AWS S3, etc.) and/or on-premises storage andmay be used by data ingestion resources 18 as part of the data ingestionprocess. Examples of this are described herein. In some examples, dataplatform 12 may not utilize long term storage 42.

The embodiments described herein can be implemented in numerous ways,including as a process; an apparatus; a system; a composition of matter;a computer program product embodied on a computer readable storagemedium; and/or a processor, such as a processor configured to executeinstructions stored on and/or provided by a memory coupled to theprocessor. In this specification, these implementations, or any otherform that the invention may take, may be referred to as techniques. Ingeneral, the order of the steps of disclosed processes may be alteredwithin the scope of the principles described herein. Unless statedotherwise, a component such as a processor or a memory described asbeing configured to perform a task may be implemented as a generalcomponent that is temporarily configured to perform the task at a giventime or a specific component that is manufactured to perform the task.As used herein, the term ‘processor’ refers to one or more devices,circuits, and/or processing cores configured to process data, such ascomputer program instructions.

In some examples, a non-transitory computer-readable medium storingcomputer-readable instructions may be provided in accordance with theprinciples described herein. The instructions, when executed by aprocessor of a computing device, may direct the processor and/orcomputing device to perform one or more operations, including one ormore of the operations described herein. Such instructions may be storedand/or transmitted using any of a variety of known computer-readablemedia.

A non-transitory computer-readable medium as referred to herein mayinclude any non-transitory storage medium that participates in providingdata (e.g., instructions) that may be read and/or executed by acomputing device (e.g., by a processor of a computing device). Forexample, a non-transitory computer-readable medium may include, but isnot limited to, any combination of non-volatile storage media and/orvolatile storage media. Exemplary non-volatile storage media include,but are not limited to, read-only memory, flash memory, a solid-statedrive, a magnetic storage device (e.g. a hard disk, a floppy disk,magnetic tape, etc.), ferroelectric random-access memory (“RAM”), and anoptical disc (e.g., a compact disc, a digital video disc, a Blu-raydisc, etc.). Exemplary volatile storage media include, but are notlimited to, RAM (e.g., dynamic RAM).

FIG. 1C illustrates an example computing device 50 that may bespecifically configured to perform one or more of the processesdescribed herein. Any of the systems, microservices, computing devices,and/or other components described herein may be implemented by computingdevice 50.

As shown in FIG. 1C, computing device 50 may include a communicationinterface 52, a processor 54, a storage device 56, and an input/output(“I/O”) module 58 communicatively connected one to another via acommunication infrastructure 60. While an exemplary computing device 50is shown in FIG. 1C, the components illustrated in FIG. 1C are notintended to be limiting. Additional or alternative components may beused in other embodiments. Components of computing device 50 shown inFIG. 1C will now be described in additional detail.

Communication interface 52 may be configured to communicate with one ormore computing devices. Examples of communication interface 52 include,without limitation, a wired network interface (such as a networkinterface card), a wireless network interface (such as a wirelessnetwork interface card), a modem, an audio/video connection, and anyother suitable interface.

Processor 54 generally represents any type or form of processing unitcapable of processing data and/or interpreting, executing, and/ordirecting execution of one or more of the instructions, processes,and/or operations described herein. Processor 54 may perform operationsby executing computer-executable instructions 62 (e.g., an application,software, code, and/or other executable data instance) stored in storagedevice 56.

Storage device 56 may include one or more data storage media, devices,or configurations and may employ any type, form, and combination of datastorage media and/or device. For example, storage device 56 may include,but is not limited to, any combination of the non-volatile media and/orvolatile media described herein. Electronic data, including datadescribed herein, may be temporarily and/or permanently stored instorage device 56. For example, data representative ofcomputer-executable instructions 62 configured to direct processor 54 toperform any of the operations described herein may be stored withinstorage device 56. In some examples, data may be arranged in one or moredatabases residing within storage device 56.

I/O module 58 may include one or more I/O modules configured to receiveuser input and provide user output. I/O module 58 may include anyhardware, firmware, software, or combination thereof supportive of inputand output capabilities. For example, I/O module 58 may include hardwareand/or software for capturing user input, including, but not limited to,a keyboard or keypad, a touchscreen component (e.g., touchscreendisplay), a receiver (e.g., an RF or infrared receiver), motion sensors,and/or one or more input buttons.

I/O module 58 may include one or more devices for presenting output to auser, including, but not limited to, a graphics engine, a display (e.g.,a display screen), one or more output drivers (e.g., display drivers),one or more audio speakers, and one or more audio drivers. In certainembodiments, I/O module 58 is configured to provide graphical data to adisplay for presentation to a user. The graphical data may berepresentative of one or more graphical user interfaces and/or any othergraphical content as may serve a particular implementation.

FIG. 1D illustrates an example implementation 100 of configuration 10.As such, one more components shown in FIG. 1D may implement one or morecomponents shown in FIG. 1A and/or FIG. 1B. In particular,implementation 100 illustrates an environment in which activities thatoccur within datacenters are modeled using data platform 12. Usingtechniques described herein, a baseline of datacenter activity can bemodeled, and deviations from that baseline can be identified asanomalous. Anomaly detection can be beneficial in a security context, acompliance context, an asset management context, a DevOps context,and/or any other data analytics context as may serve a particularimplementation.

Two example datacenters (104 and 106) are shown in FIG. 1D, and areassociated with (e.g., belong to) entities named entity A and entity B,respectively. A datacenter may include dedicated equipment (e.g., ownedand operated by entity A, or owned/leased by entity A and operatedexclusively on entity A's behalf by a third party). A datacenter canalso include cloud-based resources, such as infrastructure as a service(IaaS), platform as a service (PaaS), and/or software as a service(SaaS) elements. The techniques described herein can be used inconjunction with multiple types of datacenters, including ones whollyusing dedicated equipment, ones that are entirely cloud-based, and onesthat use a mixture of both dedicated equipment and cloud-basedresources.

Both datacenter 104 and datacenter 106 include a plurality of nodes,depicted collectively as set of nodes 108 and set of nodes 110,respectively, in FIG. 1D. These nodes may implement compute assets 16.Installed on each of the nodes are in-server/in-virtual machine(VM)/embedded in IoT device agents (e.g., agent 112), which areconfigured to collect data and report it to data platform 12 foranalysis. As described herein, agents may be small, self-containedbinaries that can be run on any appropriate platforms, includingvirtualized ones (and, as applicable, within containers). Agents maymonitor the nodes on which they execute for a variety of differentactivities, including: connection, process, user, machine, and fileactivities. Agents can be executed in user space, and can use a varietyof kernel modules (e.g., auditd, iptables, netfilter, pcap, etc.) tocollect data. Agents can be implemented in any appropriate programminglanguage, such as C or Golang, using applicable kernel APIs.

As described herein, agents can selectively report information to dataplatform 12 in varying amounts of detail and/or with variable frequency.As is also described herein, the data collected by agents may be used bydata platform 12 to create polygraphs, which are graphs of logicalentities, connected by behaviors. In some embodiments, agents reportinformation directly to data platform 12. In other embodiments, at leastsome agents provide information to a data aggregator, such as dataaggregator 114, which in turn provides information to data platform 12.The functionality of a data aggregator can be implemented as a separatebinary or other application (distinct from an agent binary), and canalso be implemented by having an agent execute in an “aggregator mode”in which the designated aggregator node acts as a Layer 7 proxy forother agents that do not have access to data platform 12. Further, achain of multiple aggregators can be used, if applicable (e.g., withagent 112 providing data to data aggregator 114, which in turn providesdata to another aggregator (not pictured) which provides data to dataplatform 12). An example way to implement an aggregator is through aprogram written in an appropriate language, such as C or Golang.

Use of an aggregator can be beneficial in sensitive environments (e.g.,involving financial or medical transactions) where various nodes aresubject to regulatory or other architectural requirements (e.g.,prohibiting a given node from communicating with systems outside ofdatacenter 104). Use of an aggregator can also help to minimize securityexposure more generally. As one example, by limiting communications withdata platform 12 to data aggregator 114, individual nodes in nodes 108need not make external network connections (e.g., via Internet 124),which can potentially expose them to compromise (e.g., by other externaldevices, such as device 118, operated by a criminal). Similarly, dataplatform 12 can provide updates, configuration information, etc., todata aggregator 114 (which in turn distributes them to nodes 108),rather than requiring nodes 108 to allow incoming connections from dataplatform 12 directly.

Another benefit of an aggregator model is that network congestion can bereduced (e.g., with a single connection being made at any given timebetween data aggregator 114 and data platform 12, rather thanpotentially many different connections being open between various ofnodes 108 and data platform 12). Similarly, network consumption can alsobe reduced (e.g., with the aggregator applying compressiontechniques/bundling data received from multiple agents).

One example way that an agent (e.g., agent 112, installed on node 116)can provide information to data aggregator 114 is via a REST API,formatted using data serialization protocols such as Apache Avro. Oneexample type of information sent by agent 112 to data aggregator 114 isstatus information. Status information may be sent by an agentperiodically (e.g., once an hour or once any other predetermined amountof time). Alternatively, status information may be sent continuously orin response to occurrence of one or more events. The status informationmay include, but is not limited to, a. an amount of event backlog (inbytes) that has not yet been transmitted, b. configuration information,c. any data loss period for which data was dropped, d. a cumulativecount of errors encountered since the agent started, e. versioninformation for the agent binary, and/or f. cumulative statistics ondata collection (e.g., number of network packets processed, newprocesses seen, etc.).

A second example type of information that may be sent by agent 112 todata aggregator 114 is event data (described in more detail herein),which may include a UTC timestamp for each event. As applicable, theagent can control the amount of data that it sends to the dataaggregator in each call (e.g., a maximum of 10 MB) by adjusting theamount of data sent to manage the conflicting goals of transmitting dataas soon as possible, and maximizing throughput. Data can also becompressed or uncompressed by the agent (as applicable) prior to sendingthe data.

Each data aggregator may run within a particular customer environment. Adata aggregator (e.g., data aggregator 114) may facilitate data routingfrom many different agents (e.g., agents executing on nodes 108) to dataplatform 12. In various embodiments, data aggregator 114 may implement aSOCKS 5 caching proxy through which agents can connect to data platform12. As applicable, data aggregator 114 can encrypt (or otherwiseobfuscate) sensitive information prior to transmitting it to dataplatform 12, and can also distribute key material to agents which canencrypt the information (as applicable). Data aggregator 114 may includea local storage, to which agents can upload data (e.g., pcap packets).The storage may have a key-value interface. The local storage can alsobe omitted, and agents configured to upload data to a cloud storage orother storage area, as applicable. Data aggregator 114 can, in someembodiments, also cache locally and distribute software upgrades,patches, or configuration information (e.g., as received from dataplatform 12).

Various examples associated with agent data collection and reportingwill now be described.

In the following example, suppose that a user (e.g., a networkadministrator) at entity A (hereinafter “user A”) has decided to beginusing the services of data platform 12. In some embodiments, user A mayaccess a web frontend (e.g., web app 120) using a computer 126 andenrolls (on behalf of entity A) an account with data platform 12. Afterenrollment is complete, user A may be presented with a set ofinstallers, pre-built and customized for the environment of entity A,that user A can download from data platform 12 and deploy on nodes 108.Examples of such installers include, but are not limited to, a Windowsexecutable file, an iOS app, a Linux package (e.g., .deb or .rpm), abinary, or a container (e.g., a Docker container). When a user (e.g., anetwork administrator) at entity B (hereinafter “user B”) also signs upfor the services of data platform 12, user B may be similarly presentedwith a set of installers that are pre-built and customized for theenvironment of entity B.

User A deploys an appropriate installer on each of nodes 108 (e.g., witha Windows executable file deployed on a Windows-based platform or aLinux package deployed on a Linux platform, as applicable). Asapplicable, the agent can be deployed in a container. Agent deploymentcan also be performed using one or more appropriate automation tools,such as Chef, Puppet, Salt, and Ansible. Deployment can also beperformed using managed/hosted container management/orchestrationframeworks such as Kubernetes, Mesos, and/or Docker Swarm.

In various embodiments, the agent may be installed in the user space(i.e., is not a kernel module), and the same binary is executed on eachnode of the same type (e.g., all Windows-based platforms have the sameWindows-based binary installed on them). An illustrative function of anagent, such as agent 112, is to collect data (e.g., associated with node116) and report it (e.g., to data aggregator 114). Other tasks that canbe performed by agents include data configuration and upgrading.

One approach to collecting data as described herein is to collectvirtually all information available about a node (and, e.g., theprocesses running on it). Alternatively, the agent may monitor fornetwork connections, and then begin collecting information aboutprocesses associated with the network connections, using the presence ofa network packet associated with a process as a trigger for collectingadditional information about the process. As an example, if a user ofnode 116 executes an application, such as a calculator application,which does not typically interact with the network, no information aboutuse of that application may be collected by agent 112 and/or sent todata aggregator 114. If, however, the user of node 116 executes an sshcommand (e.g., to ssh from node 116 to node 122), agent 112 may collectinformation about the process and provide associated information to dataaggregator 114. In various embodiments, the agent may alwayscollect/report information about certain events, such as privilegeescalation, irrespective of whether the event is associated with networkactivity.

An approach to collecting information (e.g., by an agent) is as follows,and described in conjunction with process 200 depicted in FIG. 2A. Anagent (e.g., agent 112) monitors its node (e.g., node 116) for networkactivity. One example way that agent 112 can monitor node 116 fornetwork activity is by using a network packet capture tool (e.g.,listening using libpcap). As packets are received (201), the agentobtains and maintains (e.g., in an in-memory cache) connectioninformation associated with the network activity (202). Examples of suchinformation include DNS query/response, TCP, UDP, and IP information.

The agent may also determine a process associated with the networkconnection (203). One example approach is for the agent to use a kernelnetwork diagnostic API (e.g., netlink_diag) to obtain inode/processinformation from the kernel. Another example approach is for the agentto scan using netstat (e.g., on /proc/net/tcp, /proc/net/tcp6,/proc/net/udp, and /proc/net/udp6) to obtain sockets and relate them toprocesses. Information such as socket state (e.g., whether a socket isconnected, listening, etc.) can also be collected by the agent.

One way an agent can obtain a mapping between a given inode and aprocess identifier is to scan within the /proc/pid directory. For eachof the processes currently running, the agent examines each of theirfile descriptors. If a file descriptor is a match for the inode, theagent can determine that the process associated with the file descriptorowns the inode. Once a mapping is determined between an inode and aprocess identifier, the mapping is cached. As additional packets arereceived for the connection, the cached process information is used(rather than a new search being performed).

In some cases, exhaustively scanning for an inode match across everyfile descriptor may not be feasible (e.g., due to CPU limitations). Invarious embodiments, searching through file descriptors is accordinglyoptimized. User filtering is one example of such an optimization. Agiven socket is owned by a user. Any processes associated with thesocket will be owned by the same user as the socket. When matching aninode (identified as relating to a given socket) against processes, theagent can filter through the processes and only examine the filedescriptors of processes sharing the same user owner as the socket. Invarious embodiments, processes owned by root are always searched against(e.g., even when user filtering is employed).

Another example of an optimization is to prioritize searching the filedescriptors of certain processes over others. One such prioritization isto search through the subdirectories of /proc/starting with the youngestprocess. One approximation of such a sort order is to search through/proc/in reverse order (e.g., examining highest numbered processesfirst). Higher numbered processes are more likely to be newer (i.e., notlong-standing processes), and thus more likely to be associated with newconnections (i.e., ones for which inode-process mappings are not alreadycached). In some cases, the most recently created process may not havethe highest process identifier (e.g., due to the kernel wrapping throughprocess identifiers).

Another example prioritization is to query the kernel for anidentification of the most recently created process and to search in abackward order through the directories in /proc/(e.g., starting at themost recently created process and working backwards, then wrapping tothe highest value (e.g., 32768) and continuing to work backward fromthere). An alternate approach is for the agent to keep track of thenewest process that it has reported information on (e.g., to dataaggregator 114), and begin its search of /proc/in a forward orderstarting from the PID of that process.

Another example prioritization is to maintain, for each user activelyusing node 116, a list of the five (or any other number) most recentlyactive processes. Those processes are more likely than other processes(less active, or passive) on node 116 to be involved with newconnections, and can thus be searched first. For many processes, lowervalued file descriptors tend to correspond to non-sockets (e.g., stdin,stdout, stderr). Yet another optimization is to preferentially searchhigher valued file descriptors (e.g., across processes) over lowervalued file descriptors (that are less likely to yield matches).

In some cases, while attempting to locate a process identifier for agiven inode, an agent may encounter a socket that does not correspond tothe inode being matched against and is not already cached. The identityof that socket (and its corresponding inode) can be cached, oncediscovered, thus removing a future need to search for that pair.

In some cases, a connection may terminate before the agent is able todetermine its associated process (e.g., due to a very short-livedconnection, due to a backlog in agent processing, etc.). One approach toaddressing such a situation is to asynchronously collect informationabout the connection using the audit kernel API, which streamsinformation to user space. The information collected from the audit API(which can include PID/inode information) can be matched by the agentagainst pcap/inode information. In some embodiments, the audit API isalways used, for all connections. However, due to CPU utilizationconsiderations, use of the audit API can also be reserved forshort/otherwise problematic connections (and/or omitted, as applicable).

Once the agent has determined which process is associated with thenetwork connection (203), the agent can then collect additionalinformation associated with the process (204). As will be described inmore detail below, some of the collected information may includeattributes of the process (e.g., a process parent hierarchy, and anidentification of a binary associated with the process). As will also bedescribed in more detail below, other of the collected information isderived (e.g., session summarization data and hash values).

The collected information is then transmitted (205), e.g., by an agent(e.g., agent 112) to a data aggregator (e.g., data aggregator 114),which in turn provides the information to data platform 12. In someembodiments, all information collected by an agent may be transmitted(e.g., to a data aggregator and/or to data platform 12). In otherembodiments, the amount of data transmitted may be minimized (e.g., forefficiency reasons), using various techniques.

One approach to minimizing the amount of data flowing from agents (suchas agents installed on nodes 108) to data platform 12 is to use atechnique of implicit references with unique keys. The keys can beexplicitly used by data platform 12 to extract/derive relationships, asnecessary, in a data set at a later time, without impacting performance.

As previously mentioned, some data collected about a process is constantand does not change over the lifetime of the process (e.g., attributes),and some data changes (e.g., statistical information and other variableinformation). Constant data can be transmitted (210) once, when theagent first becomes aware of the process. And, if any changes to theconstant data are detected (e.g., a process changes its parent), arefreshed version of the data can be transmitted (210) as applicable.

In some examples, an agent may collect variable data (e.g., data thatmay change over the lifetime of the process). In some examples, variabledata can be transmitted (210) at periodic (or other) intervals.Alternatively, variable data may be transmitted in substantially realtime as it is collected. In some examples, the variable data mayindicate a thread count for a process, a total virtual memory used bythe process, the total resident memory used by the process, the totaltime spent by the process executing in user space, and/or the total timespent by the process executing in kernel space. In some examples, thedata may include a hash that may be used within data platform 12 to joinprocess creation time attributes with runtime attributes to construct afull dataset.

Below are additional examples of data that an agent, such as agent 112,can collect and provide to data platform 12.

1. User Data

Core User Data: user name, UID (user ID), primary group, other groups,home directory.

Failed Login Data: IP address, hostname, username, count.

User Login Data: user name, hostname, IP address, start time, TTY(terminal), UID (user ID), GID (group ID), process, end time.

2. Machine Data

Dropped Packet Data: source IP address, destination IP address,destination port, protocol, count.

Machine Data: hostname, domain name, architecture, kernel, kernelrelease, kernel version, OS, OS version, OS description, CPU, memory,model number, number of cores, last boot time, last boot reason, tags(e.g., Cloud provider tags such as AWS, GCP, or Azure tags), defaultrouter, interface name, interface hardware address, interface IP addressand mask, promiscuous mode.

3. Network Data

Network Connection Data: source IP address, destination IP address,source port, destination port, protocol, start time, end time, incomingand outgoing bytes, source process, destination process, direction ofconnection, histograms of packet length, inter packet delay, sessionlengths, etc.

Listening Ports in Server: source IP address, port number, protocol,process.

Dropped Packet Data: source IP address, destination IP address,destination port, protocol, count.

Arp Data: source hardware address, source IP address, destinationhardware address, destination IP address.

DNS Data: source IP address, response code, response string, question(request), packet length, final answer (response).

4. Application Data

Package Data: exe path, package name, architecture, version, packagepath, checksums (MD5, SHA-1, SHA-256), size, owner, owner ID.

Application Data: command line, PID (process ID), start time, UID (userID), EUID (effective UID), PPID (parent process ID), PGID (process groupID), SID (session ID), exe path, username, container ID.

5. Container Data

Container Image Data: image creation time, parent ID, author, containertype, repo, (AWS) tags, size, virtual size, image version.

Container Data: container start time, container type, container name,container ID, network mode, privileged, PID mode, IP addresses,listening ports, volume map, process ID.

6. File Data

File path, file data hash, symbolic links, file creation data, filechange data, file metadata, file mode.

As mentioned above, an agent, such as agent 112, can be deployed in acontainer (e.g., a Docker container), and can also be used to collectinformation about containers. Collection about a container can beperformed by an agent irrespective of whether the agent is itselfdeployed in a container or not (as the agent can be deployed in acontainer running in a privileged mode that allows for monitoring).

Agents can discover containers (e.g., for monitoring) by listening forcontainer create events (e.g., provided by Docker), and can also performperiodic ordered discovery scans to determine whether containers arerunning on a node. When a container is discovered, the agent can obtainattributes of the container, e.g., using standard Docker API calls(e.g., to obtain IP addresses associated with the container, whetherthere's a server running inside, what port it is listening on,associated PIDs, etc.). Information such as the parent process thatstarted the container can also be collected, as can information aboutthe image (which comes from the Docker repository).

In various embodiments, agents may use namespaces to determine whether aprocess is associated with a container. Namespaces are a feature of theLinux kernel that can be used to isolate resources of a collection ofprocesses. Examples of namespaces include process ID (PID) namespaces,network namespaces, and user namespaces. Given a process, the agent canperform a fast lookup to determine whether the process is part of thenamespace the container claims to be its namespace.

As mentioned, agents can be configured to report certain types ofinformation (e.g., attribute information) once, when the agent firstbecomes aware of a process. In various embodiments, such staticinformation is not reported again (or is reported once a day, everytwelve hours, etc.), unless it changes (e.g., a process changes itsparent, changes its owner, or a SHA-1 of the binary associated with theprocess changes).

In contrast to static/attribute information, certain types of datachange constantly (e.g., network-related data). In various embodiments,agents are configured to report a list of current connections everyminute (or other appropriate time interval). In that connection listwill be connections that started in that minute interval, connectionsthat ended in that minute interval, and connections that were ongoingthroughout the minute interval (e.g., a one minute slice of a one hourconnection).

In various embodiments, agents are configured to collect/computestatistical information about connections (e.g., at the one minute levelof granularity and or at any other time interval). Examples of suchinformation include, for the time interval, the number of bytestransferred, and in which direction. Another example of informationcollected by an agent about a connection is the length of time betweenpackets. For connections that span multiple time intervals (e.g., aseven minute connection), statistics may be calculated for each minuteof the connection. Such statistical information (for all connections)can be reported (e.g., to a data aggregator) once a minute.

In various embodiments, agents are also configured to maintain histogramdata for a given network connection, and provide the histogram data(e.g., in the Apache Avro data exchange format) under the Connectionevent type data. Examples of such histograms include: 1. a packet lengthhistogram (packet_len_hist), which characterizes network packetdistribution; 2. a session length histogram (session_len_hist), whichcharacterizes a network session length; 3. a session time histogram(session_time_hist), which characterizes a network session time; and 4.a session switch time histogram (session_switch_time_hist), whichcharacterizes network session switch time (i.e., incoming→outgoing andvice versa). For example, histogram data may include one or more of thefollowing fields: 1. count, which provides a count of the elements inthe sampling; 2. sum, which provides a sum of elements in the sampling;3. max, which provides the highest value element in the sampling; 4.std_dev, which provides the standard deviation of elements in thesampling; and 5. buckets, which provides a discrete sample bucketdistribution of sampling data (if applicable).

For some protocols (e.g., HTTP), typically, a connection is opened, astring is sent, a string is received, and the connection is closed. Forother protocols (e.g., NFS), both sides of the connection engage in aconstant chatter. Histograms allow data platform 12 to model applicationbehavior (e.g., using machine learning techniques), for establishingbaselines, and for detecting deviations. As one example, suppose that agiven HTTP server typically sends/receives 1,000 bytes (in eachdirection) whenever a connection is made with it. If a connectiongenerates 500 bytes of traffic, or 2,000 bytes of traffic, suchconnections would be considered within the typical usage pattern of theserver. Suppose, however, that a connection is made that results in 10Gof traffic. Such a connection is anomalous and can be flaggedaccordingly.

Returning to FIG. 1D, as previously mentioned, data aggregator 114 maybe configured to provide information (e.g., collected from nodes 108 byagents) to data platform 12. Data aggregator 128 may be similarlyconfigured to provide information to data platform 12. As shown in FIG.1D, both aggregator 114 and aggregator 128 may connect to a loadbalancer 130, which accepts connections from aggregators (and/or asapplicable, agents), as well as other devices, such as computer 126(e.g., when it communicates with web app 120), and supports fairbalancing. In various embodiments, load balancer 130 is a reverse proxythat load balances accepted connections internally to variousmicroservices (described in more detail below), allowing for servicesprovided by data platform 12 to scale up as more agents are added to theenvironment and/or as more entities subscribe to services provided bydata platform 12. Example ways to implement load balancer 130 include,but are not limited to, using HaProxy, using nginx, and using elasticload balancing (ELB) services made available by Amazon.

Agent service 132 is a microservice that is responsible for acceptingdata collected from agents (e.g., provided by aggregator 114). Invarious embodiments, agent service 132 uses a standard secure protocol,such as HTTPS to communicate with aggregators (and as applicableagents), and receives data in an appropriate format such as Apache Avro.When agent service 132 receives an incoming connection, it can perform avariety of checks, such as to see whether the data is being provided bya current customer, and whether the data is being provided in anappropriate format. If the data is not appropriately formatted (and/oris not provided by a current customer), it may be rejected.

If the data is appropriately formatted, agent service 132 may facilitatecopying the received data to a streaming data stable storage using astreaming service (e.g., Amazon Kinesis and/or any other suitablestreaming service. Once the ingesting into the streaming service iscomplete, service 132 may send an acknowledgement to the data provider(e.g., data aggregator 114). If the agent does not receive such anacknowledgement, it is configured to retry sending the data to dataplatform 12. One way to implement agent service 132 is as a REST APIserver framework (e.g., Java DropWizard), configured to communicate withKinesis (e.g., using a Kinesis library).

In various embodiments, data platform 12 uses one or more streams (e.g.,Kinesis streams) for all incoming customer data (e.g., including dataprovided by data aggregator 114 and data aggregator 128), and the datais sharded based on the node (also referred to herein as a “machine”)that originated the data (e.g., node 116 vs. node 122), with each nodehaving a globally unique identifier within data platform 12. Multipleinstances of agent service 132 can write to multiple shards.

Kinesis is a streaming service with a limited period (e.g., 1-7 days).To persist data longer than a day, the data may be copied to long termstorage 42 (e.g., S3). Data loader 136 is a microservice that isresponsible for picking up data from a data stream (e.g., a Kinesisstream) and persisting it in long term storage 42. In one exampleembodiment, files collected by data loader 136 from the Kinesis streamare placed into one or more buckets, and segmented using a combinationof a customer identifier and time slice. Given a particular timesegment, and a given customer identifier, the corresponding file (storedin long term storage) contains five minutes (or another appropriate timeslice) of data collected at that specific customer from all of thecustomer's nodes. Data loader 136 can be implemented in any appropriateprogramming language, such as Java or C, and can be configured to use aKinesis library to interface with Kinesis. In various embodiments, dataloader 136 uses the Amazon Simple Queue Service (SQS) (e.g., to alert DBloader 140 that there is work for it to do).

DB loader 140 is a microservice that is responsible for loading datainto an appropriate data store 30, such as SnowflakeDB or AmazonRedshift, using individual per-customer databases. In particular, DBloader 140 is configured to periodically load data into a set of rawtables from files created by data loader 136 as per above. DB loader 140manages throughput, errors, etc., to make sure that data is loadedconsistently and continuously. Further, DB loader 140 can read incomingdata and load into data store 30 data that is not already present intables of data store 30 (also referred to herein as a database). DBloader 140 can be implemented in any appropriate programming language,such as Java or C, and an SQL framework such as jOOQ (e.g., to manageSQLs for insertion of data), and SQL/JDBC libraries. In some examples,DB loader 140 may use Amazon S3 and Amazon Simple Queue Service (SQS) tomanage files being transferred to and from data store 30.

Customer data included in data store 30 can be augmented with data fromadditional data sources, such as AWS CloudTrail and/or other types ofexternal tracking services. To this end, data platform may include atracking service analyzer 144, which is another microservice. Trackingservice analyzer 144 may pull data from an external tracking service(e.g., Amazon CloudTrail) for each applicable customer account, as soonas the data is available. Tracking service analyzer 144 may normalizethe tracking data as applicable, so that it can be inserted into datastore 30 for later querying/analysis. Tracking service analyzer 144 canbe written in any appropriate programming language, such as Java or C.Tracking service analyzer 144 also makes use of SQL/JDBC libraries tointeract with data store 30 to insert/query data.

As described herein, data platform 12 can model activities that occurwithin datacenters, such as datacenters 104 and 106. The model may bestable over time, and differences, even subtle ones (e.g., between acurrent state of the datacenter and the model) can be surfaced. Theability to surface such anomalies can be particularly beneficial indatacenter environments where rogue employees and/or external attackersmay operate slowly (e.g., over a period of months), hoping that theelastic nature of typical resource use (e.g., virtualized servers) willhelp conceal their nefarious activities.

Using techniques described herein, data platform 12 can automaticallydiscover entities (which may implement compute assets 16) deployed in agiven datacenter. Examples of entities include workloads, applications,processes, machines, virtual machines, containers, files, IP addresses,domain names, and users. The entities may be grouped together logically(into analysis groups) based on behaviors, and temporal behaviorbaselines can be established. In particular, using techniques describedherein, periodic graphs can be constructed (also referred to herein aspolygraphs), in which the nodes are applicable logical entities, and theedges represent behavioral relationships between the logical entities inthe graph. Baselines can be created for every node and edge.

Communication (e.g., between applications/nodes) is one example of abehavior. A model of communications between processes is an example of abehavioral model. As another example, the launching of applications isanother example of a behavior that can be modeled. The baselines may beperiodically updated (e.g., hourly) for every entity. Additionally oralternatively, the baselines may be continuously updated insubstantially real-time as data is collected by agents. Deviations fromthe expected normal behavior can then be detected and automaticallyreported (e.g., as anomalies or threats detected). Such deviations maybe due to a desired change, a misconfiguration, or malicious activity.As applicable, data platform 12 can score the detected deviations (e.g.,based on severity and threat posed). Additional examples of analysisgroups include models of machine communications, models of privilegechanges, and models of insider behaviors (monitoring the interactivebehavior of human users as they operate within the datacenter).

Two example types of information collected by agents are network levelinformation and process level information. As previously mentioned,agents may collect information about every connection involving theirrespective nodes. And, for each connection, information about both theserver and the client may be collected (e.g., using theconnection-to-process identification techniques described above). DNSqueries and responses may also be collected. The DNS query informationcan be used in logical entity graphing (e.g., collapsing many differentIP addresses to a single service—e.g., s3.amazon.com). Examples ofprocess level information collected by agents include attributes (userID, effective user ID, and command line). Information such as whatuser/application is responsible for launching a given process and thebinary being executed (and its SHA-256 values) may also be provided byagents.

The dataset collected by agents across a datacenter can be very large,and many resources (e.g., virtual machines, IP addresses, etc.) arerecycled very quickly. For example, an IP address and port number usedat a first point in time by a first process on a first virtual machinemay very rapidly be used (e.g., an hour later) by a differentprocess/virtual machine.

A dataset (and elements within it) can be considered at both a physicallevel, and a logical level, as illustrated in FIG. 2B. In particular,FIG. 2B illustrates an example 5-tuple of data 210 collected by anagent, represented physically (216) and logically (217). The 5-tupleincludes a source address 211, a source port 212, a destination address213, a destination port 214, and a protocol 215. In some cases, portnumbers (e.g., 212, 214) may be indicative of the nature of a connection(e.g., with certain port usage standardized). However, in many cases,and in particular in datacenters, port usage is ephemeral. For example,a Docker container can listen on an ephemeral port, which is unrelatedto the service it will run. When another Docker container starts (forthe same service), the port may well be different. Similarly,particularly in a virtualized environment, IP addresses may be recycledfrequently (and are thus also potentially ephemeral) or could be NATed,which makes identification difficult.

A physical representation of the 5-tuple is depicted in region 216. Aprocess 218 (executing on machine 219) has opened a connection tomachine 220. In particular, process 218 is in communication with process221. Information such as the number of packets exchanged between the twomachines over the respective ports can be recorded.

As previously mentioned, in a datacenter environment, portions of the5-tuple may change—potentially frequently—but still be associated withthe same behavior. Namely, one application (e.g., Apache) may frequentlybe in communication with another application (e.g., Oracle), usingephemeral datacenter resources. Further, either/both of Apache andOracle may be multi-homed. This can lead to potentially thousands of5-tuples (or more) that all correspond to Apache communicating withOracle within a datacenter. For example, Apache could be executed on asingle machine, and could also be executed across fifty machines, whichare variously spun up and down (with different IP addresses each time).An alternate representation of the 5-tuple of data 210 is depicted inregion 217, and is logical. The logical representation of the 5-tupleaggregates the 5-tuple (along with other connections between Apache andOracle having other 5-tuples) as logically representing the sameconnection. By aggregating data from raw physical connection informationinto logical connection information, using techniques described herein,a size reduction of six orders of magnitude in the data set can beachieved.

FIG. 2C depicts a portion of a logical polygraph. Suppose a datacenterhas seven instances of the application update_engine 225, executing asseven different processes on seven different machines, having sevendifferent IP addresses, and using seven different ports. The instancesof update_engine variously communicate with update.core-os.net 226,which may have a single IP address or many IP addresses itself, over theone hour time period represented in the polygraph. In the example shownin FIG. 2C, update_engine is a client, connecting to the serverupdate.core-os.net, as indicated by arrow 228.

Behaviors of the seven processes are clustered together, into a singlesummary. As indicated in region 227, statistical information about theconnections is also maintained (e.g., number of connections, histograminformation, etc.). A polygraph such as is depicted in FIG. 2C can beused to establish a baseline of behavior (e.g., at the one-hour level),allowing for the future detection of deviations from that baseline. Asone example, suppose that statistically an update_engine instancetransmits data at 11 bytes per second. If an instance were instead totransmit data at 1000 bytes per second, such behavior would represent adeviation from the baseline and could be flagged accordingly. Similarly,changes that are within the baseline (e.g., an eighth instance ofupdate_engine appears, but otherwise behaves as the other instances; orone of the seven instances disappears) are not flagged as anomalous.Further, datacenter events, such as failover, autobalancing, and A-Brefresh are unlikely to trigger false alarms in a polygraph, as at thelogical level, the behaviors remain the same.

In various embodiments, polygraph data is maintained for everyapplication in a datacenter, and such polygraph data can be combined tomake a single datacenter view across all such applications. FIG. 2Dillustrates a portion of a polygraph for a service that evidences morecomplex behaviors than are depicted in FIG. 2C. In particular, FIG. 2Dillustrates the behaviors of S3 as a service (as used by a particularcustomer datacenter). Clients within the datacenter variously connect tothe S3 service using one of five fully qualified domains (listed inregion 230). Contact with any of the domains is aggregated as contactwith S3 (as indicated in region 231). Depicted in region 232 are variouscontainers which (as clients) connect with S3. Other containers (whichdo not connect with S3) are not included. As with the polygraph portiondepicted in FIG. 2C, statistical information about the connections isknown and summarized, such as the number of bytes transferred, histograminformation, etc.

FIG. 2E illustrates a communication polygraph for a datacenter. Inparticular, the polygraph indicates a one hour summary of approximately500 virtual machines, which collectively run one million processes, andmake 100 million connections in that hour. As illustrated in FIG. 2E, apolygraph represents a drastic reduction in size (e.g., from trackinginformation on 100 million connections in an hour, to a few hundrednodes and a few hundred edges). Further, as a datacenter scales up(e.g., from using 10 virtual machines to 100 virtual machines as thedatacenter uses more workers to support existing applications), thepolygraph for the datacenter will tend to stay the same size (with the100 virtual machines clustering into the same nodes that the 10 virtualmachines previously clustered into). As new applications are added intothe datacenter, the polygraph may automatically scale to includebehaviors involving those applications.

In the particular polygraph shown in FIG. 2E, nodes generally correspondto workers, and edges correspond to communications the workers engage in(with connection activity being the behavior modeled in polygraph 235).Another example polygraph could model other behavior, such asapplication launching. The communications graphed in FIG. 2E includetraffic entering the datacenter, traffic exiting the datacenter, andtraffic that stays wholly within the datacenter (e.g., traffic betweenworkers). One example of a node included in polygraph 235 is the sshdapplication, depicted as node 236. As indicated in FIG. 2E, 421instances of sshd were executing during the one hour time period of datarepresented in polygraph 235. As indicated in region 237, nodes withinthe datacenter communicated with a total of 1349 IP addresses outside ofthe datacenter (and not otherwise accounted for, e.g., as belonging to aservice such as Amazon AWS 238 or Slack 239).

In the following examples, suppose that user B, an administrator ofdatacenter 106, is interacting with data platform 12 to viewvisualizations of polygraphs in a web browser (e.g., as served to user Bvia web app 120). One type of polygraph user B can view is anapplication-communication polygraph, which indicates, for a given onehour window (or any other suitable time interval), which applicationscommunicated with which other applications. Another type of polygraphuser B can view is an application launch polygraph. User B can also viewgraphs related to user behavior, such as an insider behavior graph whichtracks user connections (e.g., to internal and external applications,including chains of such behavior), a privilege change graph whichtracks how privileges change between processes, and a user login graph,which tracks which (logical) machines a user logs into.

FIG. 2F illustrates an example of an application-communication polygraphfor a datacenter (e.g., datacenter 106) for the one hour period of 9am-10 am on June 5. The time slice currently being viewed is indicatedin region 240. If user B clicks his mouse in region 241, user B will beshown a representation of the application-communication polygraph asgenerated for the following hour (10 am-11 am on June 5).

FIG. 2G depicts what is shown in user B's browser after he has clickedon region 241, and has further clicked on region 242. The selection inregion 242 turns on and off the ability to compare two time intervals toone another. User B can select from a variety of options when comparingthe 9 am-10 am and 10 am-11 am time intervals. By clicking region 248,user B will be shown the union of both graphs (i.e., any connectionsthat were present in either time interval). By clicking region 249, userB will be shown the intersection of both graphs (i.e., only thoseconnections that were present in both time intervals).

As shown in FIG. 2G, user B has elected to click on region 250, whichdepicts connections that are only present in the 9 am-10 am polygraph ina first color 251, and depicts connections that are only present in the10 am-11 am polygraph in a second color 252. Connections present in bothpolygraphs are omitted from display. As one example, in the 9 am-10 ampolygraph (corresponding to connections made during the 9 am-10 am timeperiod at datacenter 106), a connection was made by a server to sshd(253) and also to system (254). Both of those connections ended prior to10 am and are thus depicted in the first color. As another example, inthe 10 am-11 am polygraph (corresponding to connections made during the10 am-11 am time period at datacenter 106), a connection was made from aknown bad external IP to nginx (255). The connection was not presentduring the 9 am-10 am time slice and thus is depicted in the secondcolor. As yet another example, two different connections were made to aSlack service between 9 am and 11 am. However, the first was made by afirst client during the 9 am-10 am time slice (256) and the second wasmade by a different client during the 10 am-11 am slice (257), and sothe two connections are depicted respectively in the first and secondcolors and blue.

Returning to the polygraph depicted in FIG. 2F, suppose user B enters“etcd” into the search box located in region 244. User B will then bepresented with the interface illustrated in FIG. 2H. As shown in FIG.2H, three applications containing the term “etcd” were engaged incommunications during the 9 am-10 am window. One application is etcdctl,a command line client for etcd. As shown in FIG. 2H, a total of threedifferent etcdctl processes were executed during the 9 am-10 am window,and were clustered together (260). FIG. 2H also depicts two differentclusters that are both named etcd2. The first cluster includes (for the9 am-10 am window) five members (261) and the second cluster includes(for the same window) eight members (262). The reason for these twodistinct clusters is that the two groups of applications behavedifferently (e.g., they exhibit two distinct sets of communicationpatterns). Specifically, the instances of etcd2 in cluster 261 onlycommunicate with locksmithctl (263) and other etcd2 instances (in bothclusters 261 and 262). The instances of etcd2 in cluster 262 communicatewith additional entities, such as etcdctl and Docker containers. Asdesired, user B can click on one of the clusters (e.g., cluster 261) andbe presented with summary information about the applications included inthe cluster, as is shown in FIG. 2I (e.g., in region 265). User B canalso double click on a given cluster (e.g., cluster 261) to see detailson each of the individual members of the cluster broken out.

Suppose user B now clicks on region 245 of the interface shown in FIG.2F. User B will then be shown an application launch polygraph. Launchingan application is another example of a behavior. The launch polygraphmodels how applications are launched by other applications. FIG. 2Jillustrates an example of a portion of a launch polygraph. Inparticular, user B has typed “find” into region 266, to see how the“find” application is being launched. As shown in FIG. 2J, in the launchpolygraph for the 10 am-11 am time period, find applications (267) arealways launched by bash (268), which is in turn always launched bysystem (269). If find is launched by a different application, this wouldbe anomalous behavior.

FIG. 2K illustrates another example of a portion of an applicationlaunch polygraph. In FIG. 2K, user B has searched (270) for “python ma”to see how “python marathon_lb” (271) is launched. As shown in FIG. 2K,in each case (during the one hour time slice of 10 am-11 am), pythonmarathon_lb is launched as a result of a chain of the same sevenapplications each time. If python marathon_lb is ever launched in adifferent manner, this indicates anomalous behavior. The behavior couldbe indicative of malicious activities, but could also be due to otherreasons, such as a misconfiguration, a performance-related issue, and/ora failure, etc.

Suppose user B now clicks on region 246 of the interface shown in FIG.2F. User B will then be shown an insider behavior graph. The insiderbehavior graph tracks information about behaviors such as processesstarted by a user interactively using protocols such as ssh or telnet,and any processes started by those processes. As one example, suppose anadministrator logs into a first virtual machine in datacenter 106 (e.g.,using sshd via an external connection he makes from a hotel), using afirst set of credentials (e.g., first.last@example.com and anappropriate password). From the first virtual machine, the administratorconnects to a second virtual machine (e.g., using the same credentials),then uses the sudo command to change identities to those of anotheruser, and then launches a program. graphs built by data platform 12 canbe used to associate the administrator with each of his actions,including launching the program using the identity of another user.

FIG. 2L illustrates an example of a portion of an insider behaviorgraph. In particular, in FIG. 2L, user B is viewing a graph thatcorresponds to the time slice of 3 pm-4 pm on June 1. FIG. 2Lillustrates the internal/external applications that users connected toduring the one hour time slice. If a user typically communicates withparticular applications, that information will become part of abaseline. If the user deviates from his baseline behavior (e.g., usingnew applications, or changing privilege in anomalous ways), suchanomalies can be surfaced.

FIG. 2M illustrates an example of a portion of a privilege change graph,which identifies how privileges are changed between processes.Typically, when a user launches a process (e.g., “ls”), the processinherits the same privileges that the user has. And, while a process canhave fewer privileges than the user (i.e., go down in privilege), it israre (and generally undesirable) for a user to escalate in privilege.Information included in the privilege change graph can be determined byexamining the parent of each running process, and determining whetherthere is a match in privilege between the parent and the child. If theprivileges are different, a privilege change has occurred (whether achange up or a change down). The application ntpd is one rare example ofa scenario in which a process escalates (272) to root, and then returnsback (273). The sudo command is another example (e.g., used by anadministrator to temporarily have a higher privilege). As with the otherexamples, ntpd's privilege change actions, and the legitimate actions ofvarious administrators (e.g., using sudo) will be incorporated into abaseline model by data platform 12. When deviations occur, such as wherea new application that is not ntpd escalates privilege, or where anindividual that has not previously/does not routinely use sudo does so,such behaviors can be identified as anomalous.

FIG. 2N illustrates an example of a portion of a user login graph, whichidentifies which users log into which logical nodes. Physical nodes(whether bare metal or virtualized) are clustered into a logical machinecluster, for example, using yet another graph, a machine-server graph,an example of which is shown in FIG. 2O. For each machine, adetermination is made as to what type of machine it is, based on whatkind(s) of workflows it runs. As one example, some machines run asmaster nodes (having a typical set of workflows they run, as masternodes) and can thus be clustered as master nodes. Worker nodes aredifferent from master nodes, for example, because they run Dockercontainers, and frequently change as containers move around. Workernodes can similarly be clustered.

As previously mentioned, the polygraph depicted in FIG. 2E correspondsto activities in a datacenter in which, in a given hour, approximately500 virtual machines collectively run one million processes, and make100 million connections in that hour. The polygraph represents a drasticreduction in size (e.g., from tracking information on 100 millionconnections in an hour, to a few hundred nodes and a few hundred edges).Using techniques described herein, such a polygraph can be constructed(e.g., using commercially available computing infrastructure) in lessthan an hour (e.g., within a few minutes). Thus, ongoing hourlysnapshots of a datacenter can be created within a two hour moving window(i.e., collecting data for the time period 8 am-9 am, while alsogenerating a snapshot for the time previous time period 7 am-8 am). Thefollowing describes various example infrastructure that can be used inpolygraph construction, and also describes various techniques that canbe used to construct polygraphs.

Returning to FIG. 1D, embodiments of data platform 12 may be built usingany suitable infrastructure as a service (IaaS) (e.g., AWS). Forexample, data platform 12 can use Simple Storage Service (S3) for datastorage, Key Management Service (KMS) for managing secrets, Simple QueueService (SQS) for managing messaging between applications, Simple EmailService (SES) for sending emails, and Route 53 for managing DNS. Otherinfrastructure tools can also be used. Examples include: orchestrationtools (e.g., Kubernetes or Mesos/Marathon), service discovery tools(e.g., Mesos-DNS), service load balancing tools (e.g., marathon-LB),container tools (e.g., Docker or rkt), log/metric tools (e.g., collectd,fluentd, kibana, etc.), big data processing systems (e.g., Spark,Hadoop, AWS Redshift, Snowflake etc.), and distributed key value stores(e.g., Apache Zookeeper or etcd2).

As previously mentioned, in various embodiments, data platform 12 maymake use of a collection of microservices. Each microservice can havemultiple instances, and may be configured to recover from failure,scale, and distribute work amongst various such instances, asapplicable. For example, microservices are auto-balancing for newinstances, and can distribute workload if new instances are started orexisting instances are terminated. In various embodiments, microservicesmay be deployed as self-contained Docker containers. A Mesos-Marathon orSpark framework can be used to deploy the microservices (e.g., withMarathon monitoring and restarting failed instances of microservices asneeded). The service etcd2 can be used by microservice instances todiscover how many peer instances are running, and used for calculating ahash-based scheme for workload distribution. Microservices may beconfigured to publish various health/status metrics to either an SQSqueue, or etcd2, as applicable. In some examples, Amazon DynamoDB can beused for state management.

Additional information on various microservices used in embodiments ofdata platform 12 is provided below.

Graph generator 146 is a microservice that may be responsible forgenerating raw behavior graphs on a per customer basis periodically(e.g., once an hour). In particular, graph generator 146 may generategraphs of entities (as the nodes in the graph) and activities betweenentities (as the edges). In various embodiments, graph generator 146also performs other functions, such as aggregation, enrichment (e.g.,geolocation and threat), reverse DNS resolution, TF-IDF based commandline analysis for command type extraction, parent process tracking, etc.

Graph generator 146 may perform joins on data collected by the agents,so that both sides of a behavior are linked. For example, suppose afirst process on a first virtual machine (e.g., having a first IPaddress) communicates with a second process on a second virtual machine(e.g., having a second IP address). Respective agents on the first andsecond virtual machines may each report information on their view of thecommunication (e.g., the PID of their respective processes, the amountof data exchanged and in which direction, etc.). When graph generatorperforms a join on the data provided by both agents, the graph willinclude a node for each of the processes, and an edge indicatingcommunication between them (as well as other information, such as thedirectionality of the communication—i.e., which process acted as theserver and which as the client in the communication).

In some cases, connections are process to process (e.g., from a processon one virtual machine within the cloud environment associated withentity A to another process on a virtual machine within the cloudenvironment associated with entity A). In other cases, a process may bein communication with a node (e.g., outside of entity A) which does nothave an agent deployed upon it. As one example, a node within entity Amight be in communication with node 172, outside of entity A. In such ascenario, communications with node 172 are modeled (e.g., by graphgenerator 146) using the IP address of node 172. Similarly, where a nodewithin entity A does not have an agent deployed upon it, the IP addressof the node can be used by graph generator in modeling.

Graphs created by graph generator 146 may be written to data store 30and cached for further processing. A graph may be a summary of allactivity that happened in a particular time interval. As each graphcorresponds to a distinct period of time, different rows can beaggregated to find summary information over a larger timestamp. In someexamples, picking two different graphs from two different timestamps canbe used to compare different periods. If necessary, graph generator canparallelize its workload (e.g., where its backlog cannot otherwise behandled within a particular time period, such as an hour, or if isrequired to process a graph spanning a long time period).

Graph generator 146 can be implemented in any appropriate programminglanguage, such as Java or C, and machine learning libraries, such asSpark's MLLib. Example ways that graph generator computations can beimplemented include using SQL or Map-R, using Spark or Hadoop.

SSH tracker 148 is a microservice that may be responsible for followingssh connections and process parent hierarchies to determine trails ofuser ssh activity. Identified ssh trails are placed by the SSH tracker148 into data store 30 and cached for further processing.

SSH tracker 148 can be implemented in any appropriate programminglanguage, such as Java or C, and machine libraries, such as Spark'sMLLib. Example ways that SSH tracker computations can be implementedinclude using SQL or Map-R, using Spark or Hadoop.

Threat aggregator 150 is a microservice that may be responsible forobtaining third party threat information from various applicablesources, and making it available to other micro-services. Examples ofsuch information include reverse DNS information, GeoIP information,lists of known bad domains/IP addresses, lists of known bad files etc.As applicable, the threat information is normalized before insertioninto data store 30. Threat aggregator 150 can be implemented in anyappropriate programming language, such as Java or C, using SQL/JDBClibraries to interact with data store 30 (e.g., for insertions andqueries).

Scheduler 152 is a microservice that may act as a scheduler and that mayrun arbitrary jobs organized as a directed graph. In some examples,scheduler 152 ensures that all jobs for all customers are able to runduring at a given time interval (e.g., every hour). Scheduler 152 mayhandle errors and retrying for failed jobs, track dependencies, manageappropriate resource levels, and/or scale jobs as needed. Scheduler 152can be implemented in any appropriate programming language, such as Javaor C. A variety of components can also be used, such as open sourcescheduler frameworks (e.g., Airflow), or AWS services (e.g., the AWSData pipeline) which can be used for managing schedules.

Graph Behavior Modeler (GBM) 154 is a microservice that may computepolygraphs. In particular, GBM 154 can be used to find clusters of nodesin a graph that should be considered similar based on some set of theirproperties and relationships to other nodes. As described herein, theclusters and their relationships can be used to provide visibility intoa datacenter environment without requiring user specified labels. GBM154 may track such clusters over time persistently, allowing for changesto be detected and alerts to be generated.

GBM 154 may take as input a raw graph (e.g., as generated by graphgenerator 146). Nodes are actors of a behavior, and edges are thebehavior relationship itself. For example, in the case of communication,example actors include processes, which communicate with otherprocesses. The GBM 154 clusters the raw graph based on behaviors ofactors and produces a summary (the polygraph). The polygraph summarizesbehavior at a datacenter level. The GBM also produces “observations”that represent changes detected in the datacenter. Such observations maybe based on differences in cumulative behavior (e.g., the baseline) ofthe datacenter with its current behavior. The GBM 154 can be implementedin any appropriate programming language, such as Java, C, or Golang,using appropriate libraries (as applicable) to handle distributed graphcomputations (handling large amounts of data analysis in a short amountof time). Apache Spark is another example tool that can be used tocompute polygraphs. The GBM can also take feedback from users and adjustthe model according to that feedback. For example, if a given user isinterested in relearning behavior for a particular entity, the GBM canbe instructed to “forget” the implicated part of the polygraph.

GBM runner 156 is a microservice that may be responsible for interfacingwith GBM 154 and providing GBM 154 with raw graphs (e.g., using a querylanguage, such as SQL, to push any computations it can to data store30). GBM runner 156 may also insert polygraph output from GBM 154 todata store 30. GBM runner 156 can be implemented in any appropriateprogramming language, such as Java or C, using SQL/JDBC libraries tointeract with data store 30 to insert and query data.

Alert generator 158 is a microservice that may be responsible forgenerating alerts. Alert generator 158 may examine observations (e.g.,produced by GBM 154) in aggregate, deduplicate them, and score them.Alerts may be generated for observations with a score exceeding athreshold. Alert generator 158 may also compute (or retrieves, asapplicable) data that a customer (e.g., user A or user B) might needwhen reviewing the alert. Examples of events that can be detected bydata platform 12 (and alerted on by alert generator 158) include, butare not limited to the following:

-   -   new user: This event may be created the first time a user (e.g.,        of node 116) is first observed by an agent within a datacenter.    -   user launched new binary: This event may be generated when an        interactive user launches an application for the first time.    -   new privilege escalation: This event may be generated when user        privileges are escalated and a new application is run.    -   new application or container: This event may be generated when        an application or container is seen for the first time.    -   new external connection: This event may be generated when a        connection to an external IP/domain is made from a new        application.    -   new external host or IP: This event may be generated when a new        external host or IP is involved in a connection with a        datacenter.    -   new internal connection: This event may be generated when a        connection between internal-only applications is seen for the        first time.    -   new external client: This event may be generated when a new        external connection is seen for an application which typically        does not have external connections.    -   new parent: This event may be generated when an application is        launched by a different parent.    -   connection to known bad IP/domain: Data platform 12 maintains        (or can otherwise access) one or more reputation feeds. If an        environment makes a connection to a known bad IP or domain, an        event will be generated.    -   login from a known bad IP/domain: An event may be generated when        a successful connection to a datacenter from a known bad IP is        observed by data platform 12.

Alert generator 158 can be implemented in any appropriate programminglanguage, such as Java or C, using SQL/JDBC libraries to interact withdata store 30 to insert and query data. In various embodiments, alertgenerator 158 also uses one or more machine learning libraries, such asSpark's MLLib (e.g., to compute scoring of various observations). Alertgenerator 158 can also take feedback from users about which kinds ofevents are of interest and which to suppress.

QsJob Server 160 is a microservice that may look at all the dataproduced by data platform 12 for an hour, and compile a materializedview (MV) out of the data to make queries faster. The MV helps make surethat the queries customers most frequently run, and data that theysearch for, can be easily queried and answered. QsJob Server 160 mayalso precompute and cache a variety of different metrics so that theycan quickly be provided as answers at query time. QsJob Server 160 canbe implemented using any appropriate programming language, such as Javaor C, using SQL/JDBC libraries. In some examples, QsJob Server 160 isable to compute an MV efficiently at scale, where there could be a largenumber of joins. An SQL engine, such as Oracle, can be used toefficiently execute the SQL, as applicable.

Alert notifier 162 is a microservice that may take alerts produced byalert generator 158 and send them to customers' integrated SecurityInformation and Event Management (SIEM) products (e.g., Splunk, Slack,etc.). Alert notifier 162 can be implemented using any appropriateprogramming language, such as Java or C. Alert notifier 162 can beconfigured to use an email service (e.g., AWS SES or pagerduty) to sendemails. Alert notifier 162 may also provide templating support (e.g.,Velocity or Moustache) to manage templates and structured notificationsto STEM products.

Reporting module 164 is a microservice that may be responsible forcreating reports out of customer data (e.g., daily summaries of events,etc.) and providing those reports to customers (e.g., via email).Reporting module 164 can be implemented using any appropriateprogramming language, such as Java or C. Reporting module 164 can beconfigured to use an email service (e.g., AWS SES or pagerduty) to sendemails. Reporting module 164 may also provide templating support (e.g.,Velocity or Moustache) to manage templates (e.g., for constructingHTML-based email).

Web app 120 is a microservice that provides a user interface to datacollected and processed on data platform 12. Web app 120 may providelogin, authentication, query, data visualization, etc. features. Web app120 may, in some embodiments, include both client and server elements.Example ways the server elements can be implemented are using JavaDropWizard or Node.Js to serve business logic, and a combination ofJSON/HTTP to manage the service. Example ways the client elements can beimplemented are using frameworks such as React, Angular, or Backbone.JSON, jQuery, and JavaScript libraries (e.g., underscore) can also beused.

Query service 166 is a microservice that may manage all database accessfor web app 120. Query service 166 abstracts out data obtained from datastore 30 and provides a JSON-based REST API service to web app 120.Query service 166 may generate SQL queries for the REST APIs that itreceives at run time. Query service 166 can be implemented using anyappropriate programming language, such as Java or C and SQL/JDBClibraries, or an SQL framework such as jOOQ. Query service 166 caninternally make use of a variety of types of databases, including arelational database engine 168 (e.g., AWS Aurora) and/or data store 30to manage data for clients. Examples of tables that query service 166manages are OLTP tables and data warehousing tables.

Cache 170 may be implemented by Redis and/or any other service thatprovides a key-value store. Data platform 12 can use cache 170 to keepinformation for frontend services about users. Examples of suchinformation include valid tokens for a customer, valid cookies ofcustomers, the last time a customer tried to login, etc.

FIG. 3A illustrates an example of a process for detecting anomalies in anetwork environment. In various embodiments, process 300 is performed bydata platform 12. The process begins at 301 when data associated withactivities occurring in a network environment (such as entity A'sdatacenter) is received. One example of such data that can be receivedat 301 is agent-collected data described above (e.g., in conjunctionwith process 200).

At 302, a logical graph model is generated, using at least a portion ofthe monitored activities. A variety of approaches can be used togenerate such logical graph models, and a variety of logical graphs canbe generated (whether using the same, or different approaches). Thefollowing is one example of how data received at 301 can be used togenerate and maintain a model.

During bootstrap, data platform 12 creates an aggregate graph ofphysical connections (also referred to herein as an aggregated physicalgraph) by matching connections that occurred in the first hour intocommunication pairs. Clustering is then performed on the communicationpairs. Examples of such clustering, described in more detail below,include performing Matching Neighbor clustering and similarity (e.g.,SimRank) clustering. Additional processing can also be performed (and isdescribed in more detail below), such as by splitting clusters based onapplication type, and annotating nodes with DNS query information. Theresulting graph (also referred to herein as a base graph or commongraph) can be used to generate a variety of models, where a subset ofnode and edge types (described in more detail below) and theirproperties are considered in a given model. One example of a model is aUID to UID model (also referred to herein as a Uid2Uid model) whichclusters together processes that share a username and show similarprivilege change behavior. Another example of a model is a CType model,which clusters together processes that share command line similarity.Yet another example of a model is a PType model, which clusters togetherprocesses that share behaviors over time.

Each hour (or any other predetermined time interval) after bootstrap, anew snapshot is taken (i.e., data collected about a datacenter in thelast hour is processed) and information from the new snapshot is mergedwith existing data to create and (as additional data iscollected/processed) maintain a cumulative graph. The cumulative graph(also referred to herein as a cumulative PType graph and a polygraph) isa running model of how processes behave over time. Nodes in thecumulative graph are PType nodes, and provide information such as a listof all active processes and PIDs in the last hour, the number ofhistoric total processes, the average number of active processes perhour, the application type of the process (e.g., the CType of thePType), and historic CType information/frequency. Edges in thecumulative graph can represent connectivity and provide information suchas connectivity frequency. The edges can be weighted (e.g., based onnumber of connections, number of bytes exchanged, etc.). Edges in thecumulative graph (and snapshots) can also represent transitions.

One approach to merging a snapshot of the activity of the last hour intoa cumulative graph is as follows. An aggregate graph of physicalconnections is made for the connections included in the snapshot (as waspreviously done for the original snapshot used during bootstrap). And,clustering/splitting is similarly performed on the snapshot's aggregategraph. Next, PType clusters in the snapshot's graph are compared againstPType clusters in the cumulative graph to identify commonality.

One approach to determining commonality is, for any two nodes that aremembers of a given CmdType (described in more detail below), comparinginternal neighbors and calculating a set membership Jaccard distance.The pairs of nodes are then ordered by decreasing similarity (i.e., withthe most similar sets first). For nodes with a threshold amount ofcommonality (e.g., at least 66% members in common), any new nodes (i.e.,appearing in the snapshot's graph but not the cumulative graph) areassigned the same PType identifier as is assigned to the correspondingnode in the cumulative graph. For each node that is not classified(i.e., has not been assigned a PType identifier), a network signature isgenerated (i.e., indicative of the kinds of network connections the nodemakes, who the node communicates with, etc.). The following processingis then performed until convergence. If a match of the network signatureis found in the cumulative graph, the unclassified node is assigned thePType identifier of the corresponding node in the cumulative graph. Anynodes which remain unclassified after convergence are new PTypes and areassigned new identifiers and added to the cumulative graph as new. Asapplicable, the detection of a new PType can be used to generate analert. If the new PType has a new CmdType, a severity of the alert canbe increased. If any surviving nodes (i.e., present in both thecumulative graph and the snapshot graph) change PTypes, such change isnoted as a transition, and an alert can be generated. Further, if asurviving node changes PType and also changes CmdType, a severity of thealert can be increased.

Changes to the cumulative graph (e.g., a new PType or a new edge betweentwo PTypes) can be used (e.g., at 303) to detect anomalies (described inmore detail below). Two example kinds of anomalies that can be detectedby data platform 12 include security anomalies (e.g., a user or processbehaving in an unexpected manner) and devops/root cause anomalies (e.g.,network congestion, application failure, etc.). Detected anomalies canbe recorded and surfaced (e.g., to administrators, auditors, etc.), suchas through alerts which are generated at 304 based on anomaly detection.

Additional detail regarding processing performed, by various componentsdepicted in FIG. 1D (whether performed individually or in combination),in conjunction with model/polygraph construction (e.g., as performed at302) are provided below.

As explained above, an aggregated physical graph can be generated on aper customer basis periodically (e.g., once an hour) from raw physicalgraph information, by matching connections (e.g., between two processeson two virtual machines). In various embodiments, a deterministic fixedapproach is used to cluster nodes in the aggregated physical graph(e.g., representing processes and their communications). As one example,Matching Neighbors Clustering (MNC) can be performed on the aggregatedphysical graph to determine which entities exhibit identical behaviorand cluster such entities together.

FIG. 3B depicts a set of example processes (p1, p2, p3, and p4)communicating with other processes (p10 and p11). FIG. 3B is a graphicalrepresentation of a small portion of an aggregated physical graphshowing (for a given time period, such as an hour) which processes in adatacenter communicate with which other processes. Using MNC, processesp1, p2, and p3 will be clustered together (305), as they exhibitidentical behavior (they communicate with p10 and only p10). Process p4,which communicates with both p10 and p11, will be clustered separately.

In MNC, only those processes exhibiting identical (communication)behavior will be clustered. In various embodiments, an alternateclustering approach can also/instead be used, which uses a similaritymeasure (e.g., constrained by a threshold value, such as a 60%similarity) to cluster items. In some embodiments, the output of MNC isused as input to SimRank, in other embodiments, MNC is omitted.

FIG. 3C depicts a set of example processes (p4, p5, p6) communicatingwith other processes (p7, p8, p9). As illustrated, most of nodes p4, p5,and p6 communicate with most of nodes p7, p8, and p9 (as indicated inFIG. 3C with solid connection lines). As one example, process p4communicates with process p7 (310), process p8 (311), and process p9(312). An exception is process p6, which communicates with processes p7and p8, but does not communicate with process p9 (as indicated by dashedline 313). If MNC were applied to the nodes depicted in FIG. 3C, nodesp4 and p5 would be clustered (and node p6 would not be included in theircluster).

One approach to similarity clustering is to use SimRank. In anembodiment of the SimRank approach, for a given node v in a directedgraph, I(v) and O(v) denote the respective set of in-neighbors andout-neighbors of v. Individual in-neighbors are denoted as I_(i)(v), for1≤i≤|I(v)|, and individual out-neighbors are denoted as O_(i)(v), for1≤i≤|O(v)|. The similarity between two objects a and b can be denoted bys(a,b)∈[1,0]. A recursive equation (hereinafter “the SimRank equation”)can be written for s(a,b), where, if a=b, then s(a,b) is defined as 1,otherwise,

${s\left( {a,b} \right)} = {\frac{C}{❘{{I(a)}{❘{❘{I(b)}❘}}}}{\sum_{i = 1}^{❘{I(a)}❘}{\sum_{j = 1}^{❘{I(b)}❘}{s\left( {{I_{i}(a)},{I_{j}(b)}} \right)}}}}$where C is a constant between 0 and 1. One example value for the decayfactor C is 0.8 (and a fixed number of iterations such as five). Anotherexample value for the decay factor C is 0.6 (and/or a different numberof iterations). In the event that a or b has no in-neighbors, similarityis set to s(a,b)=0, so the summation is defined to be 0 when I(a)=Ø orI(b)=Ø.

The SimRank equations for a graph G can be solved by iteration to afixed point. Suppose n is the number of nodes in G. For each iterationk, n² entries s_(k)(*,*) are kept, where s_(k)(a,b) gives the scorebetween a and b on iteration k. Successive computations of s_(k+1)(*,*)are made based on s_(k)(*,*). Starting with s₀(*,*), where each s₀(a,b)is a lower bound on the actual SimRank score s(a,b):

${s_{0}\left( {a,b} \right)} = \left\{ \begin{matrix}{1,} & {{{{if}a} = b},} \\{0,} & {{{if}a} \neq {b.}}\end{matrix} \right.$

The SimRank equation can be used to compute s_(k+1)(a,b) from s_(k)(*,*)with

${s_{k + 1}\left( {a,b} \right)} = {\frac{C}{{❘{I(a)}❘}{❘{I(b)}❘}}{\sum_{i = 1}^{❘{I(a)}❘}{\sum_{j = 1}^{❘{I(b)}❘}{s_{k}\left( {{I_{i}(a)},{I_{j}(b)}} \right)}}}}$for a≠b, and s_(k+1)(a,b)=1 for a=b. On each iteration k+1, thesimilarity of (a,b) is updated using the similarity scores of theneighbors of (a,b) from the previous iteration k according to theSimRank equation. The values s_(k)(*,*) are nondecreasing as kincreases.

Returning to FIG. 3C, while MNC would cluster nodes p4 and p5 together(and not include node p6 in their cluster), application of SimRank wouldcluster nodes p4-p6 into one cluster (314) and also cluster nodes p7-p9into another cluster (315).

FIG. 3D depicts a set of processes, and in particular server processess1 and s2, and client processes c1, c2, c3, c4, c5, and c6. Suppose onlynodes s1, s2, c1, and c2 are present in the graph depicted in FIG. 3D(and the other nodes depicted are omitted from consideration). UsingMNC, nodes s1 and s2 would be clustered together, as would nodes c1 andc2. Performing SimRank clustering as described above would also resultin those two clusters (s1 and s2, and c1 and c2). As previouslymentioned, in MNC, identical behavior is required. Thus, if node c3 werenow also present in the graph, MNC would not include c3 in a clusterwith c2 and c1 because node c3 only communicates with node s2 and notnode s1. In contrast, a SimRank clustering of a graph that includesnodes s1, s2, c1, c2, and c3 would result (based, e.g., on an applicableselected decay value and number of iterations) in a first clustercomprising nodes s1 and s2, and a second cluster of c1, c2, and c3. Asan increasing number of nodes which communicate with server process s2,and do not also communicate with server process s1, are included in thegraph (e.g., as c4, c5, and c6 are added), under SimRank, nodes s1 ands2 will become decreasingly similar (i.e., their intersection isreduced).

In various embodiments, SimRank is modified (from what is describedabove) to accommodate differences between the asymmetry of client andserver connections. As one example, SimRank can be modified to usedifferent thresholds for client communications (e.g., an 80% match amongnodes c1-c6) and for server communications (e.g., a 60% match amongnodes s1 and s2). Such modification can also help achieve convergence insituations such as where a server process dies on one node and restartson another node.

The application of MNC/SimRank to an aggregated physical graph resultsin a smaller graph, in which processes which are determined to besufficiently similar are clustered together. Typically, clustersgenerated as output of MNC will be underinclusive. For example, for thenodes depicted in FIG. 3C, process p6 will not be included in a clusterwith processes p4 and p5, despite substantial similarity in theircommunication behaviors. The application of SimRank (e.g., to the outputof MNC) helps mitigate the underinclusiveness of MNC, but can result inoverly inclusive clusters. As one example, suppose (returning to thenodes depicted in FIG. 3B) that as a result of applying SimRank to thedepicted nodes, nodes p1-p4 are all included in a single cluster. BothMNC and SimRank operate agnostically of which application a givenprocess belongs to. Suppose processes p1-p3 each correspond to a firstapplication (e.g., an update engine), and process p4 corresponds to asecond application (e.g., sshd). Further suppose process p10 correspondsto contact with AWS. Clustering all four of the processes together(e.g., as a result of SimRank) could be problematic, particularly in asecurity context (e.g., where granular information useful in detectingthreats would be lost).

As previously mentioned, data platform 12 may maintain a mapping betweenprocesses and the applications to which they belong. In variousembodiments, the output of SimRank (e.g., SimRank clusters) is splitbased on the applications to which cluster members belong (such a splitis also referred to herein as a “CmdType split”). If all cluster membersshare a common application, the cluster remains. If different clustermembers originate from different applications, the cluster members aresplit along application-type (CmdType) lines. Using the nodes depictedin FIG. 3D as an example, suppose that nodes c1, c2, c3, and c5 allshare “update engine” as the type of application to which they belong(sharing a CmdType). Suppose that node c4 belongs to “ssh,” and supposethat node c6 belongs to “bash.” As a result of SimRank, all six nodes(c1-c6) might be clustered into a single cluster. After a CmdType splitis performed on the cluster, however, the single cluster will be brokeninto three clusters (c1, c2, c3, c5; c4; and c6). Specifically, theresulting clusters comprise processes associated with the same type ofapplication, which exhibit similar behaviors (e.g., communicationbehaviors). Each of the three clusters resulting from the CmdType splitrepresents, respectively, a node (also referred to herein as a PType) ofa particular CmdType. Each PType is given a persistent identifier andstored persistently as a cumulative graph.

A variety of approaches can be used to determine a CmdType for a givenprocess. As one example, for some applications (e.g., sshd), aone-to-one mapping exists between the CmdType and the application/binaryname. Thus, processes corresponding to the execution of sshd will beclassified using a CmdType of sshd. In various embodiments, a list ofcommon application/binary names (e.g., sshd, apache, etc.) is maintainedby data platform 12 and manually curated as applicable. Other types ofapplications (e.g., Java, Python, and Ruby) are multi-homed, meaningthat several very different applications may all execute using thebinary name, “java.” For these types of applications, information suchas command line/execution path information can be used in determining aCmdType. In particular, the subapplication can be used as the CmdType ofthe application, and/or term frequency analysis (e.g., TF/IDF) can beused on command line information to group, for example, any marathonrelated applications together (e.g., as a python.marathon CmdType) andseparately from other Python applications (e.g., as a python.airflowCmdType).

In various embodiments, machine learning techniques are used todetermine a CmdType. The CmdType model is constrained such that theexecution path for each CmdType is unique. One example approach tomaking a CmdType model is a random forest based approach. An initialCmdType model is bootstrapped using process parameters (e.g., availablewithin one minute of process startup) obtained using one hour ofinformation for a given customer (e.g., entity A). Examples of suchparameters include the command line of the process, the command line ofthe process's parent(s) (if applicable), the uptime of the process,UID/EUID and any change information, TTY and any change information,listening ports, and children (if any). Another approach is to performterm frequency clustering over command line information to convertcommand lines into cluster identifiers.

The random forest model can be used (e.g., in subsequent hours) topredict a CmdType for a process (e.g., based on features of theprocess). If a match is found, the process can be assigned the matchingCmdType. If a match is not found, a comparison between features of theprocess and its nearest CmdType (e.g., as determined using a Levensteindistance) can be performed. The existing CmdType can be expanded toinclude the process, or, as applicable, a new CmdType can be created(and other actions taken, such as generating an alert). Another approachto handling processes which do not match an existing CmdType is todesignate such processes as unclassified, and once an hour, create a newrandom forest seeded with process information from a sampling ofclassified processes (e.g., 10 or 100 processes per CmdType) and the newprocesses. If a given new process winds up in an existing set, theprocess is given the corresponding CmdType. If a new cluster is created,a new CmdType can be created.

Conceptually, a polygraph represents the smallest possible graph ofclusters that preserve a set of rules (e.g., in which nodes included inthe cluster must share a CmdType and behavior). As a result ofperforming MNC, SimRank, and cluster splitting (e.g., CmdType splitting)many processes are clustered together based on commonality of behavior(e.g., communication behavior) and commonality of application type. Suchclustering represents a significant reduction in graph size (e.g.,compared to the original raw physical graph). Nonetheless, furtherclustering can be performed (e.g., by iterating on the graph data usingthe GBM to achieve such a polygraph). As more information within thegraph is correlated, more nodes can be clustered together, reducing thesize of the graph, until convergence is reached and no furtherclustering is possible.

FIG. 3E depicts two pairs of clusters. In particular, cluster 320represents a set of client processes sharing the same CmdType (“a1”),communicating (collectively) with a server process having a CmdType(“a2”). Cluster 322 also represents a set of client processes having aCmdType a1 communicating with a server process having a CmdType a2. Thenodes in clusters 320 and 322 (and similarly nodes in 321 and 323)remain separately clustered (as depicted) after MNC/SimRank/CmdTypesplitting—isolated islands. One reason this could occur is where serverprocess 321 corresponds to processes executing on a first machine(having an IP address of 1.1.1.1). The machine fails and a new serverprocess 323 starts, on a second machine (having an IP address of2.2.2.2) and takes over for process 321.

Communications between a cluster of nodes (e.g., nodes of cluster 320)and the first IP address can be considered different behavior fromcommunications between the same set of nodes and the second IP address,and thus communications 324 and 325 will not be combined by MNC/SimRankin various embodiments. Nonetheless, it could be desirable for nodes ofclusters 320/322 to be combined (into cluster 326), and for nodes ofclusters 321/323 to be combined (into cluster 327), as representing(collectively) communications between a1 and a2. One task that can beperformed by data platform 12 is to use DNS query information to map IPaddresses to logical entities. As will be described in more detailbelow, GBM 154 can make use of the DNS query information to determinethat graph nodes of cluster 320 and graph nodes of cluster 322 both madeDNS queries for “appserverabc.example.com,” which first resolved to1.1.1.1 and then to 2.2.2.2, and to combine nodes 320/322 and 321/323together into a single pair of nodes (326 communicating with 327).

In various embodiments, GBM 154 operates in a batch manner in which itreceives as input the nodes and edges of a graph for a particular timeperiod along with its previous state, and generates as output clusterednodes, cluster membership edges, cluster-to-cluster edges, events, andits next state.

GBM 154 may not try to consider all types of entities and theirrelationships that may be available in a conceptual common graph all atonce. Instead, GBM uses a concept of models where a subset of node andedge types and their properties are considered in a given model. Such anapproach is helpful for scalability, and also to help preserve detailedinformation (of particular importance in a security context)—asclustering entities in a more complex and larger graph could result inless useful results. In particular, such an approach allows fordifferent types of relationships between entities to be preserved/moreeasily analyzed.

While GBM 154 can be used with different models corresponding todifferent subgraphs, core abstractions remain the same across types ofmodels.

For example, each node type in a GBM model is considered to belong to aclass. The class can be thought of as a way for the GBM to split nodesbased on the criteria it uses for the model. The class for a node isrepresented as a string whose value is derived from the node's key andproperties depending on the GBM Model. Note that different GBM modelsmay create different class values for the same node. For each node typein a given GBM model, GBM 154 can generate clusters of nodes for thattype. A GBM generated cluster for a given member node type cannot spanmore than one class for that node type. GBM 154 generates edges betweenclusters that have the same types as the edges between source anddestination cluster node types.

Additionally or alternatively, the processes described herein as beingused for a particular model can be used (can be the same) across models,and different models can also be configured with different settings.

Additionally or alternatively, the node types and the edge types maycorrespond to existing types in the common graph node and edge tablesbut this is not necessary. Even when there is a correspondence, theproperties provided to GBM 154 are not limited to the properties thatare stored in the corresponding graph table entries. They can beenriched with additional information before being passed to GBM 154.

Logically, the input for a GBM model can be characterized in a mannerthat is similar to other graphs. Edge triplets can be expressed, forexample, as an array of source node type, edge type, and destinationnode type. And, each node type is associated with node properties, andeach edge type is associated with edge properties. Other edge tripletscan also be used (and/or edge triplets can be extended) in accordancewith various embodiments.

Note that the physical input to the GBM model need not (and does not, invarious embodiments) conform to the logical input. For example, theedges in the PtypeConn model correspond to edges between MatchingNeighbors (MN) clusters, where each process node has an MN clusteridentifier property. In the User ID to User ID model (also referred toherein as the Uid2Uid model), edges are not explicitly providedseparately from nodes (as the euid array in the node properties servesthe same purpose). In both cases, however, the physical informationprovides the applicable information necessary for the logical input.

The state input for a particular GBM model can be stored in a file, adatabase, or other appropriate storage. The state file (from a previousrun) is provided, along with graph data, except for when the first runfor a given model is performed, or the model is reset. In some cases, nodata may be available for a particular model in a given time period, andGBM may not be run for that time period. As data becomes available at afuture time, GBM can run using the latest state file as input.

GBM 154 outputs cluster nodes, cluster membership edges, andinter-cluster relationship edges that are stored (in some embodiments)in the graph node tables: node_c, node_cm, and node_icr, respectively.The type names of nodes and edges may conform to the following rules:

-   -   A given node type can be used in multiple different GBM models.        The type names of the cluster nodes generated by two such models        for that node type will be different. For instance, process type        nodes will appear in both PtypeConn and Uid2Uid models, but        their cluster nodes will have different type names.    -   The membership edge type name is “MemberOf.”    -   The edge type names for cluster-to-cluster edges will be the        same as the edge type names in the underlying node-to-node edges        in the input.

The following are example events GBM 154 can generate: new class, newcluster, new edge from class to class, split class (the notion that GBM154 considers all nodes of a given type and class to be in the samecluster initially and if GBM 154 splits them into multiple clusters, itis splitting a class), new edge from cluster and class, new edge betweencluster and cluster, and/or new edge from class to cluster.

One underlying node or edge in the logical input can cause multipletypes of events to be generated. Conversely, one event can correspond tomultiple nodes or edges in the input. Not every model generates everyevent type.

Additional information regarding examples of data structures/models thatcan be used in conjunction with models used by data platform 12 is nowprovided.

In some examples, a PTypeConn Model clusters nodes of the same classthat have similar connectivity relationships. For example, if twoprocesses had similar incoming neighbors of the same class and outgoingneighbors of the same class, they could be clustered.

The node input to the PTypeConn model for a given time period includesnon-interactive (i.e., not associated with tty) process nodes that hadconnections in the time period and the base graph nodes of other types(IP Service Endpoint (IPSep) comprising an IP address and a port), DNSService Endpoint (DNSSep) and IPAddress) that have been involved inthose connections. The base relationship is the connectivityrelationship for the following type triplets:

-   -   Process, ConnectedTo, Process    -   Process, ConnectedTo, IP Service Endpoint (IPSep)    -   Process, ConnectedTo, DNS Service Endpoint (DNSSep)    -   IPAddress, ConnectedTo, ProcessProcess, DNS, ConnectedTo,        Process

The edge inputs to this model are the ConnectedTo edges from the MNcluster, instead of individual node-to-node ConnectedTo edges from thebase graph. The membership edges created by this model refer to the basegraph node type provided in the input.

Class Values:

The class values of nodes are determined as follows depending on thenode type (e.g., Process nodes, IPSep nodes, DNSSep nodes, and IPAddress nodes).

Process nodes: if exe_path contains java then “java <cmdline_term_1> . ..” else if exe_path contains python then “python <cmdline_term_1> . . .”else “last_part_of_exe_path” IPSep nodes: if IP_internal then “IntIPS”else if severity = 0 then “<IP_addr>:<protocol>:<port>” else“<IP_addr>:<port>_BadIP” DNSSep nodes: if IP_internal = 1 then“<hostname>” else if severity = 0 then “<hostname>:<protocol>:port” else“<hostname>:<port>_BadIP” IP Address nodes (will appear only on clientside): if IP_internal = 1 then “IPIntC” else if severity = 0 then“ExtIPC” else “ExtBadIPC”

Events:

A new class event in this model for a process node is equivalent toseeing a new CType being involved in a connection for the first time.Note that this does not mean the CType was not seen before. It ispossible that it was previously seen but did not make a connection atthat time.

A new class event in this model for an IPSep node with IP_internal=0 isequivalent to seeing a connection to a new external IP address for thefirst time.

A new class event in this model for a DNSSep node is equivalent toseeing a connection to a new domain for the first time.

A new class event in this model for an IPAddress node with IP_internal=0and severity=0 is equivalent to seeing a connection from any external IPaddress for the first time.

A new class event in this model for an IPAddress node with IP_internal=0and severity >0 is equivalent to seeing a connection from any badexternal IP address for the first time.

A new class to class to edge from a class for a process node to a classfor a process node is equivalent to seeing a communication from thesource CType making a connection to the destination CType for the firsttime.

A new class to class to edge from a class for a process node to a classfor a DNSSep node is equivalent to seeing a communication from thesource CType making a connection to the destination domain name for thefirst time.

An IntPConn Model may be similar to the PtypeConn Model, except thatconnection edges between parent/child processes and connections betweenprocesses where both sides are not interactive are filtered out.

A Uid2Uid Model may cluster processes with the same username that showsimilar privilege change behavior. For instance, if two processes withthe same username had similar effective user values, launched processeswith similar usernames, and were launched by processes with similarusernames, then they could be clustered.

An edge between a source cluster and destination cluster generated bythis model means that all of the processes in the source cluster had aprivilege change relationship to at least one process in the destinationcluster.

The node input to this model for a given time period includes processnodes that are running in that period. The value of a class of processnodes is “<username>”.

The base relationship that is used for clustering is privilege change,either by the process changing its effective user ID, or by launching achild process which runs with a different user.

The physical input for this model includes process nodes (only), withthe caveat that the complete ancestor hierarchy of process nodes active(i.e., running) for a given time period is provided as input even if anancestor is not active in that time period. Note that effective user IDsof a process are represented as an array in the process node properties,and launch relationships are available from ppid_hash fields in theproperties as well.

A new class event in this model is equivalent to seeing a user for thefirst time.

A new class to class edge event is equivalent to seeing the source usermaking a privilege change to the destination user for the first time.

A Ct2Ct Model may cluster processes with the same CType that showsimilar launch behavior. For instance, if two processes with the sameCType have launched processes with similar CTypes, then they could beclustered.

The node input to this model for a given time period includes processnodes that are running in that period. The value class of process nodesis CType (similar to how it is created for the PtypeConn Model).

The base relationship that is used for clustering is a parent processwith a given CType launching a child process with another givendestination CType.

The physical input for this model includes process nodes (only) with thecaveat that the complete ancestor hierarchy active process nodes (i.e.,that are running) for a given time period is provided as input even ifan ancestor is not active in that time period. Note that launchrelationships are available from ppid_hash fields in the process nodeproperties.

An edge between a source cluster and destination cluster generated bythis model means that all of the processes in the source clusterlaunched at least one process in the destination cluster.

A new class event in this model is equivalent to seeing a CType for thefirst time. Note that the same type of event will be generated by thePtypeConn Model as well.

A new class to class edge event is equivalent to seeing the source CTypelaunching the destination CType for the first time.

An MTypeConn Model may cluster nodes of the same class that have similarconnectivity relationships. For example, if two machines had similarincoming neighbors of the same class and outgoing neighbors of the sameclass, they could be clustered.

A new class event in this model will be generated for external IPaddresses or (as applicable) domain names seen for the first time. Notethat a new class to class to edge Machine, class to class for an IPSepor DNSName node will also be generated at the same time.

The membership edges generated by this model will refer to Machine,IPAddress, DNSName, and IPSep nodes in the base graph. Though the nodesprovided to this model are IPAddress nodes instead of IPSep nodes, themembership edges it generates will refer to IPSep type nodes.Alternatively, the base graph can generate edges between Machine andIPSep node types. Note that the Machine to IPAddress edges havetcp_dst_ports/udp_dst_ports properties that can be used for thispurpose.

The node input to this model for a given time period includes machinenodes that had connections in the time period and the base graph nodesof other types (IPAddress and DNSName) that were involved in thoseconnections.

The base relationship is the connectivity relationship for the followingtype triplets:

-   -   Machine, ConnectedTo, Machine    -   Machine, ConnectedTo, IPAddress    -   Machine, ConnectedTo, DNSName    -   IPAddress, ConnectedTo, Machine, DNS, ConnectedTo, Machine

The edge inputs to this model are the corresponding ConnectedTo edges inthe base graph.

Class Values:

-   -   Machine:

The class value for all Machine nodes is “Machine.”

The machine_terms property in the Machine nodes is used, in variousembodiments, for labeling machines that are clustered together. If amajority of the machines clustered together share a term in themachine_terms, that term can be used for labeling the cluster.

-   -   IPSep:

The class value for IPSep nodes is determined as follows:

if IP_internal then “IntIPS” else if severity = 0 then“<ip_addr>:<protocol>:<port>” else “<IP_addr_BadIP>”  IP Address:

The class value for IpAddress nodes is determined as follows:

if IP_internal then “IntIPC” else if severity = 0 then “ExtIPC” else“ExtBadIPC”

-   -   DNSName:

The class value for DNSName nodes is determined as follows:

if severity = 0 then “<hostname>” else then “<hostname>_BadIP”

An example structure for a New Class Event is now described.

The key field for this event type looks as follows (using the PtypeConnmodel as an example):

{  “node”: {  “class”: {  “cid”: “httpd”  },  “key”: {  “cid”: “29654” },  “type”: “PtypeConn”  } }

It contains the class value and also the ID of the cluster where thatclass value is observed. Multiple clusters can be observed with the samevalue in a given time period. It contains the class value and also theID of the cluster where that class value is observed. Multiple clusterscan be observed with the same value in a given time period. Accordingly,in some embodiments, GBM 154 generates multiple events of this type forthe same class value.

The properties field looks as follows:

{  “set_size”: 5 }

The set size indicates the size of the cluster referenced in the keysfield.

Conditions:

For a given model and time period, multiple NewClass events can begenerated if there is more than one cluster in that class. NewNodeevents will not be generated separately in this case.

Example New Class to Class Edge Event Structure:

The key field for this event type looks as follows (using the PtypeConnmodel as an example):

 “edge”: {  “dst_node”: {  “class”: {  “cid”: “java war”  },  “key”: { “cid”: “27635”  },  “type”: “PtypeConn”  },  “src_node”: {  “class”: { “cid”: “IntIPC”  },  “key”: {  “cid”: “20881”  },  “type”: “PtypeConn” },  “type”: “ConnectedTo”  } }

The key field contains source and destination class values and alsosource and destination cluster identifiers (i.e., thesrc/dst_node:key.cid represents the src/dst cluster identifier).

In a given time period for a given model, an event of this type couldinvolve multiple edges between different cluster pairs that have thesame source and destination class values. GBM 154 can generate multipleevents in this case with different source and destination clusteridentifiers.

The props fields look as follows for this event type:

{  “dst_set_size”: 2,  “src_set_size”: 1 }

The source and destination sizes represent the sizes of the clustersgiven in the keys field.

Conditions:

For a given model and time period, multiple NewClassToClass events canbe generated if there are more than one pair of clusters in that classpair. NewNodeToNode events are not generated separately in this case.

Combining Events at the Class Level: for a given model and time period,the following example types of events can represent multiple changes inthe underlying GBM cluster level graph in terms of multiple new clustersor multiple new edges between clusters:

-   -   NewClass    -   NewEdgeClassToClass    -   NewEdgeNodeToClass    -   NewEdgeClassToNode

Multiple NewClass events with the same model and class can be output ifthere are multiple clusters in that new class.

Multiple NewEdgeClassToClass events with the same model and class paircan be output if there are multiple new cluster edges within that classpair.

Multiple NewEdgeNodeToClass events with the same model and destinationclass can be output if there are multiple new edges from the sourcecluster to the destination clusters in that destination class (the firsttime seeing this class as a destination cluster class for the sourcecluster).

Multiple NewEdgeClassToNode events with the same model and source classcan be output if there are multiple new edges from source clusters tothe destination clusters in that source class (the first time seeingthis class as a source cluster class for the destination cluster).

These events may be combined at the class level and treated as a singleevent when it is desirable to view changes at the class level, e.g.,when one wants to know when there is a new CType.

In some examples, different models may have partial overlap in the typesof nodes they use from the base graph. Therefore, they can generateNewClass type events for the same class. NewClass events can also becombined across models when it is desirable to view changes at the classlevel.

Using techniques herein, actions can be associated with processes and(e.g., by associating processes with users) actions can thus also beassociated with extended user sessions. Such information can be used totrack user behavior correctly, even where a malicious user attempts tohide his trail by changing user identities (e.g., through lateralmovement). Extended user session tracking can also be useful inoperational use cases without malicious intent, e.g., where users makeoriginal logins with distinct usernames (e.g., “charlie” or “dave”) butthen perform actions under a common username (e.g., “admin” or“support”). One such example is where multiple users with administratorprivileges exist, and they need to gain superuser privilege to perform aparticular type of maintenance. It may be desirable to know whichoperations are performed (as the superuser) by which original user whendebugging issues. In the following examples describing extended usersession tracking, reference is generally made to using the secure shell(ssh) protocol as implemented by openssh (on the server side) as themechanism for logins. However, extended user session tracking is notlimited to the ssh protocol or a particular limitation and thetechniques described herein can be extended to other login mechanisms.

On any given machine, there will be a process that listens for andaccepts ssh connections on a given port. This process can run theopenssh server program running in daemon mode or it could be runninganother program (e.g., initd on a Linux system). In either case, a newprocess running openssh will be created for every new ssh login sessionand this process can be used to identify an ssh session on that machine.This process is called the “privileged” process in openssh.

After authentication of the ssh session, when an ssh client requests ashell or any other program to be run under that ssh session, a newprocess that runs that program will be created under (i.e., as a childof) the associated privileged process. If an ssh client requests portforwarding to be performed, the connections will be associated with theprivileged process.

In modern operating systems such as Linux and Windows, each process hasa parent process (except for the very first process) and when a newprocess is created the parent process is known. By tracking theparent-child hierarchy of processes, one can determine if a particularprocess is a descendant of a privileged openssh process and thus if itis associated with an ssh login session.

For user session tracking across machines (or on a single machine withmultiple logins) in a distributed environment, it is established whentwo login sessions have a parent-child relationship. After that, the“original” login session, if any, for any given login session can bedetermined by following the parent relationship recursively.

FIG. 3F is a representation of a user logging into a first machine andthen into a second machine from the first machine, as well asinformation associated with such actions. In the example of FIG. 3F, auser, Charlie, logs into Machine A (331) from a first IP address (332).As part of the login process, he provides a username (333). Onceconnected to Machine A, an openssh privileged process (334) is createdto handle the connection for the user, and a terminal session is createdand a bash process (335) is created as a child. Charlie launches an sshclient (336) from the shell, and uses it to connect (337) to Machine B(338). As with the connection he makes to Machine A, Charlie'sconnection to Machine B will have an associated incoming IP address(339), in this case, the IP address of Machine A. And, as part of thelogin process with Machine B, Charlie will provide a username (340)which need not be the same as username 333. An openssh privilegedprocess (341) is created to handle the connection, and a terminalsession and child bash process (342) will be created. From the commandline of Machine B, Charlie launches a curl command (343), which opens anHTTP connection (2028) to an external Machine C (345).

FIG. 3G is an alternate representation of actions occurring in FIG. 3F,where events occurring on Machine A are indicated along line 350, andevents occurring on Machine B are indicated along line 351. As shown inFIG. 3G, an incoming ssh connection is received at Machine A (352).Charlie logs in (as user “x”) and an ssh privileged process is createdto handle Charlie's connection (353). A terminal session is created anda bash process is created (354) as a child of process 353. Charlie wantsto ssh to Machine B, and so executes an ssh client on Machine A (355),providing credentials (as user “y”) at 356. Charlie logs into Machine B,and an ssh privileged process is created to handle Charlie's connection(357). A terminal session is created and a bash process is created (358)as a child of process 357. Charlie then executes curl (359) to downloadcontent from an external domain (via connection 360).

The external domain could be a malicious domain, or it could be benign.Suppose the external domain is malicious (and, e.g., Charlie hasmalicious intent). It would be advantageous (e.g., for security reasons)to be able to trace the contact with the external domain back to MachineA, and then back to Charlie's IP address. Using techniques describedherein (e.g., by correlating process information collected by variousagents), such tracking of Charlie's activities back to his originallogin (330) can be accomplished. In particular, an extended user sessioncan be tracked that associates Charlie's ssh processes together with asingle original login and thus original user.

As described herein, software agents (such as agent 112) may run onmachines (such as a machine that implements one of nodes 116) and detectnew connections, processes, and/or logins. As also previously explained,such agents send associated records to data platform 12 which includesone or more datastores (e.g., data store 30) for persistently storingsuch data. Such data can be modeled using logical tables, also persistedin datastores (e.g., in a relational database that provides an SQLinterface), allowing for querying of the data. Other datastores such asgraph oriented databases and/or hybrid schemes can also be used.

The following identifiers are commonly used in the tables:

-   -   MID    -   PID_hash

An ssh login session can be identified uniquely by an (MID, PID_hash)tuple. The MID is a machine identifier that is unique to each machine,whether physical or virtual, across time and space. Operating systemsuse numbers called process identifiers (PIDs) to identify processesrunning at a given time. Over time processes may die and new processesmay be started on a machine or the machine itself may restart. The PIDis not necessarily unique across time in that the same PID value can bereused for different processes at different times. In order to trackprocess descendants across time, one should therefore account for timeas well. In order to be able to identify a process on a machine uniquelyacross time, another number called a PID_hash is generated for theprocess. In various embodiments, the PID_hash is generated using acollision-resistant hash function that takes the PID, start time, and(in various embodiments, as applicable) other properties of a process.

Input data collected by agents comprises the input data model and isrepresented by the following logical tables:

-   -   connections    -   processes    -   logins

A connections table may maintain records of TCP/IP connections observedon each machine. Example columns included in a connections table are asfollows:

Column Name Description MID Identifier of the machine that theconnection was observed on. start_time Connection start time. PID_hashIdentifier of the process that was associated with the connection.src_IP_addr Source IP address (the connection was initiated from this IPaddress). src_port Source port. dst_IP_addr Destination IP address (theconnection was made to this IP address). dst_port Destination port. ProtProtocol (TCP or UDP). Dir Direction of the connection (incoming oroutgoing) with respect to this machine.

The source fields (IP address and port) correspond to the side fromwhich the connection was initiated. On the destination side, the agentassociates an ssh connection with the privileged ssh process that iscreated for that connection.

For each connection in the system, there will be two records in thetable, assuming that the machines on both sides of the connectioncapture the connection. These records can be matched based on equalityof the tuple (src_IP_addr, src_port, dst_IP_addr, dst_port, Prot) andproximity of the start_time fields (e.g., with a one minute upperthreshold between the start_time fields).

A processes table maintains records of processes observed on eachmachine. It may have the following columns:

Column Name Description MID Identifier of the machine that the processwas observed on. PID_hash Identifier of the process. start_time Starttime of the process. exe_path The executable path of the process.PPID_hash Identifier of the parent process.

A logins table may maintain records of logins to machines. It may havethe following columns:

Column Name Description MID Identifier of the machine that the login wasobserved on. sshd_PID_hash Identifier of the sshd privileged processassociated with login. login_time Time of login. login_username Usernameused in login.

Output data generated by session tracking is represented with thefollowing logical tables:

-   -   login-local-descendant    -   login-connection    -   login-lineage

Using data in these tables, it is possible to determine descendantprocesses of a given ssh login session across the environment (i.e.,spanning machines). Conversely, given a process, it is possible todetermine if it is an ssh login descendant as well as the original sshlogin session for it if so.

A login-local-descendant table maintains the local (i.e., on the samemachine) descendant processes of each ssh login session. It may have thefollowing columns:

Column Name Description MID Identifier of the machine that the login wasobserved on. sshd_PID_hash Identifier of the sshd privileged processassociated with login. login_time Time of login. login_username Usernameused in login.

A login-connections table may maintain the connections associated withssh logins. It may have the following columns:

Column Name Description MID Identifier of the machine that the processwas observed on. sshd_PID_hash Identifier of the sshd privileged processassociated with the login. login_time Time of login. login_username Theusername used in the login. src_IP_addr Source IP address (connectionwas initiated from this IP address). src_port Source port. dst_IP_addrDestination IP address (connection was made to this IP address).dst_port Destination port.

A login-lineage table may maintain the lineage of ssh login sessions. Itmay have the following columns:

Column Name Description MID Identifier of the machine that the ssh loginwas observed on. sshd_PID_hash Identifier of the sshd privileged processassociated with the login. parent_MID Identifier of the machine that theparent ssh login was observed on. parent_sshd_PID_hash Identifier of thesshd privileged process associated with the parent login. origin_MIDIdentifier of the machine that the origin ssh login was observed on.origin_sshd_PID_hash Identifier of the sshd privileged processassociated with the origin login.

The parent_MID and parent_sshd_PID_hash columns can be null if there isno parent ssh login. In that case, the (MID, sshd_PID_hash) tuple willbe the same as the (origin_MID, origin sshd_PID_hash) tuple.

FIG. 3H illustrates an example of a process for performing extended usertracking. In various embodiments, process 361 is performed by dataplatform 12. The process begins at 362 when data associated withactivities occurring in a network environment (such as entity A'sdatacenter) is received. One example of such data that can be receivedat 362 is agent-collected data described above (e.g., in conjunctionwith process 200). At 363, the received network activity is used toidentify user login activity. And, at 364, a logical graph that linksthe user login activity to at least one user and at least one process isgenerated (or updated, as applicable). Additional detail regardingprocess 361, and in particular, portions 363 and 364 of process 361 aredescribed in more detail below (e.g., in conjunction with discussion ofFIG. 3J).

FIG. 3I depicts a representation of a user logging into a first machine,then into a second machine from the first machine, and then making anexternal connection. The scenario depicted in FIG. 3I is used todescribe an example of processing that can be performed on datacollected by agents to generate extended user session trackinginformation. FIG. 3I is an alternate depiction of the information shownin FIGS. 3F and 3G.

At time t1 (365), a first ssh connection is made to Machine A (366) froman external source (367) by a user having a username of “X.” In thefollowing example, suppose the external source has an IP address of1.1.1.10 and uses source port 10000 to connect to Machine A (which hasan IP address of 2.2.2.20 and a destination port 22). External source367 is considered an external source because its IP address is outsideof the environment being monitored (e.g., is a node outside of entityA's datacenter, connecting to a node inside of entity A's datacenter).

A first ssh login session LS1 is created on machine A for user X. Theprivileged openssh process for this login is A1 (368). Under the loginsession LS1, the user creates a bash shell process with PID_hash A2(369).

At time t2 (370), inside the bash shell process A2, the user runs an sshprogram under a new process A3 (371) to log in to machine B (372) with adifferent username (“Y”). In particular, an ssh connection is made fromsource IP address 2.2.2.20 and source port 10001 (Machine A's sourceinformation) to destination IP address 2.2.2.21 and destination port 22(Machine B's destination information).

A second ssh login session LS2 is created on machine B for user Y. Theprivileged openssh process for this login is B1 (373). Under the loginsession LS2, the user creates a bash shell process with PID_hash B2(374).

At time t3 (376), inside the bash shell process B2, the user runs a curlcommand under a new process B3 (377) to download a file from an externaldestination (378). In particular, an HTTPS connection is made fromsource IP address 2.2.2.21 and source port 10002 (Machine B's sourceinformation) to external destination IP address 3.3.3.30 and destinationport 443 (the external destination's information).

Using techniques described herein, it is possible to determine theoriginal user who initiated the connection to external destination 378,which in this example is a user having the username X on machine A(where the extended user session can be determined to start with sshlogin session LS1).

Based on local descendant tracking, the following determinations can beon machine A and B without yet having performed additional processing(described in more detail below):

-   -   A3 is a descendant of A1 and thus associated with LS1.    -   The connection to the external domain from machine B is        initiated by B3.    -   B3 is a descendant of B1 and is thus associated with LS2.    -   Connection to the external domain is thus associated with LS2.

An association between A3 and LS2 can be established based on the factthat LS2 was created based on an ssh connection initiated from A3.Accordingly, it can be determined that LS2 is a child of LS1.

To determine the user responsible for making the connection to theexternal destination (e.g., if it were a known bad destination), first,the process that made the connection would be traced, i.e., from B3 toLS2. Then LS2 would be traced to LS1 (i.e., LS1 is the origin loginsession for LS2). Thus the user for this connection is the user for LS1,i.e., X. As represented in FIG. 3I, one can visualize the tracing byfollowing the links (in the reverse direction of arrows) from externaldestination 378 to A1 (368).

In the example scenario, it is assumed that both ssh connections occurin the same analysis period. However, the approaches described hereinwill also work for connections and processes that are created indifferent time periods.

FIG. 3J illustrates an example of a process for performing extended usertracking. In various embodiments, process 380 is performed periodically(e.g., once an hour in a batch fashion) by ssh tracker 148 to generatenew output data. In general, batch processing allows for efficientanalysis of large volumes of data. However, the approach can be adapted,as applicable, to process input data on a record-by-record fashion whilemaintaining the same logical data processing flow. As applicable theresults of a given portion of process 380 are stored for use in asubsequent portion.

The process begins at 381 when new ssh connection records areidentified. In particular, new ssh connections started during thecurrent time period are identified by querying the connections table.The query uses filters on the start_time and dst_port columns. Thevalues of the range filter on the start_time column are based on thecurrent time period. The dst_port column is checked against sshlistening port(s). By default, the ssh listening port number is 22.However, as this could vary across environments, the port(s) thatopenssh servers are listening to in the environment can be determined bydata collection agents dynamically and used as the filter value for thedst_port as applicable. In the scenario depicted in FIG. 3I, the queryresult will generate the records shown in FIG. 3K. Note that for theconnection between machine A and B, the two machines are likely toreport start_time values that are not exactly the same but close enoughto be considered matching (e.g., within one minute or anotherappropriate amount of time). In the above table, they are shown to bethe same for simplicity.

At 382, ssh connection records reported from source and destinationsides of the same connection are matched. The ssh connection records(e.g., returned from the query at 381) are matched based on thefollowing criteria:

-   -   The five tuples (src_IP, dst_IP, IP_prot, src_port, dst_port) of        the connection records must match.    -   The delta between the start times of the connections must be        within a limit that would account for the worst case clock        difference expected between two machines in the environment and        typical connection setup latency.    -   If there are multiple matches possible, then the match with the        smallest time delta is chosen.

Note that record 390 from machine A for the incoming connection from theexternal source cannot be matched with another record as there is anagent only on the destination side for this connection. Example outputof portion 382 of process 380 is shown in FIG. 3L. The values in thedst_PID_hash column (391) are that of the sshd privileged processassociated with ssh logins.

At 383, new logins during the current time period are identified byquerying the logins table. The query uses a range filter on thelogin_time column with values based on the current time period. In theexample depicted in FIG. 3I, the query result will generate the recordsdepicted in FIG. 3M.

At 384, matched ssh connection records created at 382 and new loginrecords created at 383 are joined to create new records that willeventually be stored in the login-connection table. The join conditionis that dst_MID of the matched connection record is equal to the MID ofthe login record and the dst_PID_hash of the matched connection recordis equal to the sshd_PID_hash of the login record. In the exampledepicted in FIG. 3I, the processing performed at 384 will generate therecords depicted in FIG. 3N.

At 385, login-local-descendant records in the lookback time period areidentified. It is possible that a process that is created in a previoustime period makes an ssh connection in the current analysis batchperiod. Although not depicted in the example illustrated in FIG. 3I,consider a case where bash process A2 does not create ssh process A3right away but instead that the ssh connection A3 later makes to machineB is processed in a subsequent time period than the one where A2 wasprocessed. While processing this subsequent time period in whichprocesses A3 and B1 are seen, knowledge of A2 would be useful inestablishing that B1 is associated with A3 (via ssh connection) which isassociated with A2 (via process parentage) which in turn would be usefulin establishing that the parent of the second ssh login is the first sshlogin. The time period for which look back is performed can be limitedto reduce the amount of historical data that is considered. However,this is not a requirement (and the amount of look back can bedetermined, e.g., based on available processing resources). The loginlocal descendants in the lookback time period can be identified byquerying the login-local-descendant table. The query uses a range filteron the login_time column where the range is fromstart_time_of_current_period−lookback_time tostart_time_of_current_period. (No records as a result of performing 385on the scenario depicted in FIG. 3I are obtained, as only a single timeperiod is applicable in the example scenario.)

At 386, new processes that are started in the current time period areidentified by querying the processes table. The query uses a rangefilter on the start_time column with values based on the current timeperiod. In the example depicted in FIG. 3I, the processing performed at386 will generate the records depicted in FIG. 3O.

At 387, new login-local-descendant records are identified. The purposeis to determine whether any of the new processes in the current timeperiod are descendants of an ssh login process and if so to createrecords that will be stored in the login-local-descendant table forthem. In order to do so, the parent-child relationships between theprocesses are recursively followed. Either a top down or bottom upapproach can be used. In a top down approach, the ssh local descendantsin the lookback period identified at 385, along with new ssh loginprocesses in the current period identified at 384 are considered aspossible ancestors for the new processes in the current periodidentified at 386.

Conceptually, the recursive approach can be considered to includemultiple sub-steps where new processes that are identified to be sshlocal descendants in the current sub-step are considered as ancestorsfor the next step. In the example scenario depicted in FIG. 3I, thefollowing descendancy relationships will be established in twosub-steps:

Sub-Step 1:

Process A2 is a local descendant of LS1 (i.e., MID=A, sshd_PID_hash=A1)because it is a child of process A1 which is the login process for LS1.

Process B2 is a local descendant of LS2 (i.e., MID=B, sshd_PID_hash=B1)because it is a child of process B1 which is the login process for LS2.

Sub-Step 2:

Process A3 is a local descendant of LS1 because it is a child of processA2 which is associated to LS1 in sub-step 1.

Process B3 is a local descendant of LS2 because it is a child of processB1 which is associated to LS2 in sub-step 1.

Implementation portion 387 can use a datastore that supports recursivequery capabilities, or, queries can be constructed to process multipleconceptual sub-steps at once. In the example depicted in FIG. 3I, theprocessing performed at 387 will generate the records depicted in FIG.3P. Note that the ssh privileged processes associated with the loginsare also included as they are part of the login session.

At 388, the lineage of new ssh logins created in the current time periodis determined by associating their ssh connections to source processesthat may be descendants of other ssh logins (which may have been createdin the current period or previous time periods). In order to do so,first an attempt is made to join the new ssh login connections in thecurrent period (identified at 384) with the combination of the loginlocal descendants in the lookback period (identified at 385) and thelogin local descendants in the current time period (identified at 386).This will create adjacency relationships between child and parentlogins. In the example depicted in FIG. 3I, the second ssh loginconnection will be associated with process A3 and an adjacencyrelationship between the two login sessions will be created (asillustrated in FIG. 3Q).

Next, the adjacency relationships are used to find the original loginsessions. While not shown in the sample scenario, there could bemultiple ssh logins in a chain in the current time period, in which casea recursive approach (as in 387) could be used. At the conclusion ofportion 388, the login lineage records depicted in FIG. 3R will begenerated.

Finally, at 389, output data is generated. In particular, the newlogin-connection, login-local-descendant, and login-lineage recordsgenerated at 384, 387, and 388 are inserted into their respective outputtables (e.g., in a transaction manner).

An alternate approach to matching TCP connections between machinesrunning an agent is for the client to generate a connection GUID andsend it in the connection request (e.g., the SYN packet) it sends andfor the server to extract the GUID from the request. If two connectionrecords from two machines have the same GUID, they are for the sameconnection. Both the client and server will store the GUID (if itexists) in the connection records they maintain and report. On theclient side, the agent can configure the network stack (e.g., using IPtables functionality on Linux) to intercept an outgoing TCP SYN packetand modify it to add the generated GUID as a TCP option. On the serverside, the agent already extracts TCP SYN packets and thus can look forthis option and extract the GUID if it exists.

Example graph-based user tracking and threat detection embodimentsassociated with data platform 12 will now be described. Administratorsand other users of network environments (e.g., entity A's datacenter104) often change roles to perform tasks. As one example, suppose thatat the start of a workday, an administrator (hereinafter “Joe Smith”)logs in to a console, using an individualized account (e.g.,username=joe.smith). Joe performs various tasks as himself (e.g.,answering emails, generating status reports, writing code, etc.). Forother tasks (e.g., performing updates), Joe may requiredifferent/additional permission than his individual account has (e.g.,root privileges). One way Joe can gain access to such permissions is byusing sudo, which will allow Joe to run a single command with rootprivileges. Another way Joe can gain access to such permissions is by suor otherwise logging into a shell as root. After gaining rootprivileges, another thing that Joe can do is switch identities. As oneexample, to perform administrative tasks, Joe may use “su help” or “sudatabase-admin” to become (respectively) the help user or thedatabase-admin user on a system. He may also connect from one machine toanother, potentially changing identities along the way (e.g., logging inas joe.smith at a first console, and connecting to a database server asdatabase-admin). When he's completed various administrative tasks, Joecan relinquish his root privileges by closing out of any additionalshells created, reverting back to a shell created for user joe.smith.

While there are many legitimate reasons for Joe to change his identitythroughout the day, such changes may also correspond to nefariousactivity. Joe himself may be nefarious, or Joe's account (joe.smith) mayhave been compromised by a third party (whether an “outsider” outside ofentity A's network, or an “insider”). Using techniques described herein,the behavior of users of the environment can be tracked (includingacross multiple accounts and/or multiple machines) and modeled (e.g.,using various graphs described herein). Such models can be used togenerate alerts (e.g., to anomalous user behavior). Such models can alsobe used forensically, e.g., helping an investigator visualize variousaspects of a network and activities that have occurred, and to attributeparticular types of actions (e.g., network connections or file accesses)to specific users.

In a typical day in a datacenter, a user (e.g., Joe Smith) will log in,run various processes, and (optionally) log out. The user will typicallylog in from the same set of IP addresses, from IP addresses within thesame geographical area (e.g., city or country), or from historicallyknown IP addresses/geographical areas (i.e., ones the user haspreviously/occasionally used). A deviation from the user's typical (orhistorical) behavior indicates a change in login behavior. However, itdoes not necessarily mean that a breach has occurred. Once logged into adatacenter, a user may take a variety of actions. As a first example, auser might execute a binary/script. Such binary/script might communicatewith other nodes in the datacenter, or outside of the datacenter, andtransfer data to the user (e.g., executing “curl” to obtain data from aservice external to the datacenter). As a second example, the user cansimilarly transfer data (e.g., out of the datacenter), such as by usingPOST. As a third example, a user might change privilege (one or moretimes), at which point the user can send/receive data as per above. As afourth example, a user might connect to a different machine within thedatacenter (one or more times), at which point the user can send/receivedata as per the above.

In various embodiments, the above information associated with userbehavior is broken into four tiers. The tiers represent example types ofinformation that data platform 12 can use in modeling user behavior:

-   -   1. The user's entry point (e.g., domains, IP addresses, and/or        geolocation information such as country/city) from which a user        logs in.    -   2. The login user and machine class.    -   3. Binaries, executables, processes, etc. a user launches.    -   4. Internal servers with which the user (or any of the user's        processes, child processes, etc.) communicates, and external        contacts (e.g., domains, IP addresses, and/or geolocation        information such as country/city) with which the user        communicates (i.e., transfers data).

In the event of a security breach, being able to concretely answerquestions about such information can be very important. And,collectively, such information is useful in providing an end-to-end path(e.g., for performing investigations).

In the following example, suppose a user (“UserA”) logs into a machine(“Machine01”) from a first IP address (“IP01”). Machine01 is inside adatacenter. UserA then launches a script (“runnable.sh”) on Machine01.From Machine01, UserA next logs into a second machine (“Machine02”) viassh, also as UserA, also within the datacenter. On Machine02, UserAagain launches a script (“new_runnable.sh”). On Machine02, UserA thenchanges privilege, becoming root on Machine02. From Machine02, UserA(now as root) logs into a third machine (“Machine03”) in the datacentervia ssh, as root on Machine03. As root on Machine03, the user executes ascript (“collect_data.sh”) on Machine03. The script internallycommunicates (as root) to a MySQL-based service internal to thedatacenter, and downloads data from the MySQL-based service. Finally, asroot on Machine03, the user externally communicates with a serveroutside the datacenter (“External01”), using a POST command. Tosummarize what has occurred, in this example, the source/entry point isIP01. Data is transferred to an external server External01. The machineperforming the transfer to External01 is Machine03. The usertransferring the data is “root” (on Machine03), while the actual user(hiding behind root) is UserA.

In the above scenario, the “original user” (ultimately responsible fortransmitting data to External01) is UserA, who logged in from IP01. Eachof the processes ultimately started by UserA, whether started at thecommand line (tty) such as “runnable.sh” or started after an sshconnection such as “new_runnable.sh,” and whether as UserA, or as asubsequent identity, are all examples of child processes which can bearranged into a process hierarchy.

As previously mentioned, machines can be clustered together logicallyinto machine clusters. One approach to clustering is to classifymachines based on information such as the types of services theyprovide/binaries they have installed upon them/processes they execute.Machines sharing a given machine class (as they share commonbinaries/services/etc.) will behave similarly to one another. Eachmachine in a datacenter can be assigned to a machine cluster, and eachmachine cluster can be assigned an identifier (also referred to hereinas a machine class). One or more tags can also be assigned to a givenmachine class (e.g., database_servers_west or prod_web_frontend). Oneapproach to assigning a tag to a machine class is to apply termfrequency analysis (e.g., TF/IDF) to the applications run by a givenmachine class, selecting as tags those most unique to the class. Dataplatform 12 can use behavioral baselines taken for a class of machinesto identify deviations from the baseline (e.g., by a particular machinein the class).

FIG. 3S illustrates an example of a process for detecting anomalies. Invarious embodiments, process 392 is performed by data platform 12. Asexplained above, a given session will have an original user. And, eachaction taken by the original user can be tied back to the original user,despite privilege changes and/or lateral movement throughout adatacenter. Process 392 begins at 393 when log data associated with auser session (and thus an original user) is received. At 394, a logicalgraph is generated, using at least a portion of the collected data. Whenan anomaly is detected (395), it can be recorded, and as applicable, analert is generated (396). The following are examples of graphs that canbe generated (e.g., at 394), with corresponding examples of anomaliesthat can be detected (e.g., at 395) and alerted upon (e.g., at 396).

FIG. 4A illustrates a representation of an embodiment of an insiderbehavior graph. In the example of FIG. 4A, each node in the graph canbe: (1) a cluster of users; (2) a cluster of launched processes; (3) acluster of processes/servers running on a machine class; (4) a clusterof external IP addresses (of incoming clients); or (5) a cluster ofexternal servers based on DNS/IP/etc. As depicted in FIG. 4A, graph datais vertically tiered into four tiers. Tier 0 (400) corresponds to entrypoint information (e.g., domains, IP addresses, and/or geolocationinformation) associated with a client entering the datacenter from anexternal entry point. Entry points are clustered together based on suchinformation. Tier 1 (401) corresponds to a user on a machine class, witha given user on a given machine class represented as a node. Tier 2(402) corresponds to launched processes, child processes, and/orinteractive processes. Processes for a given user and having similarconnectivity (e.g., sharing the processes they launch and the machineswith which they communicate) are grouped into nodes. Finally, Tier 3(403) corresponds to the services/servers/domains/IP addresses withwhich processes communicate. A relationship between the tiers can bestated as follows: Tier 0 nodes log in to tier 1 nodes. Tier 1 nodeslaunch tier 2 nodes. Tier 2 nodes connect to tier 3 nodes.

The inclusion of an original user in both Tier 1 and Tier 2 allows forhorizontal tiering. Such horizontal tiering ensures that there is nooverlap between any two users in Tier 1 and Tier 2. Such lack of overlapprovides for faster searching of an end-to-end path (e.g., one startingwith a Tier 0 node and terminating at a Tier 3 node). Horizontal tieringalso helps in establishing baseline insider behavior. For example, bybuilding an hourly insider behavior graph, new edges/changes in edgesbetween nodes in Tier 1 and Tier 2 can be identified. Any such changescorrespond to a change associated with the original user. And, any suchchanges can be surfaced as anomalous and alerts can be generated.

As explained above, Tier 1 corresponds to a user (e.g., user “U”)logging into a machine having a particular machine class (e.g., machineclass “M”). Tier 2 is a cluster of processes having command linesimilarity (e.g., CType “C”), having an original user “U,” and runningas a particular effective user (e.g., user “U1”). The value of U1 may bethe same as U (e.g., joe.smith in both cases), or the value of U1 may bedifferent (e.g., U=joe.smith and U1=root). Thus, while an edge may bepresent from a Tier 1 node to a Tier 2 node, the effective user in theTier 2 node may or may not match the original user (while the originaluser in the Tier 2 node will match the original user in the Tier 1node).

A change from a user U into a user U1 can take place in a variety ofways. Examples include where U becomes U1 on the same machine (e.g., viasu), and also where U sshes to other machine(s). In both situations, Ucan perform multiple changes, and can combine approaches. For example, Ucan become U1 on a first machine, ssh to a second machine (as U1),become U2 on the second machine, and ssh to a third machine (whether asuser U2 or user U3). In various embodiments, the complexity of how userU ultimately becomes U3 (or U5, etc.) is hidden from a viewer of aninsider behavior graph, and only an original user (e.g., U) and theeffective user of a given node (e.g., U5) are depicted. As applicable(e.g., if desired by a viewer of the insider behavior graph), additionaldetail about the path (e.g., an end-to-end path of edges from user U touser U5) can be surfaced (e.g., via user interactions with nodes).

FIG. 4B illustrates an example of a portion of an insider behavior graph(e.g., as rendered in a web browser). In the example shown, node 405(the external IP address, 52.32.40.231) is an example of a Tier 0 node,and represents an entry point into a datacenter. As indicated bydirectional arrows 406 and 407, two users, “aruneliprod” and“harishprod,” both made use of the source IP 52.32.40.231 when loggingin between 5 μm and 6 pm on Sunday July 30 (408). Nodes 409 and 410 areexamples of Tier 1 nodes, having aruneliprod and harishprod asassociated respective original users. As previously mentioned, Tier 1nodes correspond to a combination of a user and a machine class. In theexample depicted in FIG. 4B, the machine class associated with nodes 409and 410 is hidden from view to simplify visualization, but can besurfaced to a viewer of interface 404 (e.g., when the user clicks onnode 409 or 410).

Nodes 414-423 are examples of Tier 2 nodes—processes that are launchedby users in Tier 1 and their child, grandchild, etc. processes. Notethat also depicted in FIG. 4B is a Tier 1 node 411 that corresponds to auser, “root,” that logged in to a machine cluster from within thedatacenter (i.e., has an entry point within the datacenter). Nodes 425-1and 425-2 are examples of Tier 3 nodes—internal/external IP addresses,servers, etc., with which Tier 2 nodes communicate.

In the example shown in FIG. 4B, a viewer of interface 404 has clickedon node 423. As indicated in region 426, the user running the marathoncontainer is “root.” However, by following the directional arrows in thegraph backwards from node 423 (i.e. from right to left), the viewer candetermine that the original user, responsible for node 423, is“aruneli_prod,” who logged into the datacenter from IP 52.32.40.231.

The following are examples of changes that can be tracked using aninsider behavior graph model:

-   -   A user logs in from a new IP address.    -   A user logs in from a geolocation not previously used by that        user.    -   A user logs into a new machine class.    -   A user launches a process not previously used by that user.    -   A user connects to an internal server to which the user has not        previously connected.    -   An original user communicates with an external server (or        external server known to be malicious) with which that user has        not previously communicated.    -   A user communicates with an external server which has a        geolocation not previously used by that user.

Such changes can be surfaced as alerts, e.g., to help an administratordetermine when/what anomalous behavior occurs within a datacenter.Further, the behavior graph model can be used (e.g., during forensicanalysis) to answer questions helpful during an investigation. Examplesof such questions include:

-   -   Was there any new login activity (Tier 0) in the timeframe being        investigated? As one example, has a user logged in from an IP        address with unknown geolocation information? Similarly, has a        user started communicating externally with a new Tier 3 node        (e.g., one with unknown geolocation information).    -   Has there been any suspicious login activity (Tier 0) in the        timeframe being investigated? As one example, has a user logged        in from an IP address that corresponds to a known bad IP address        as maintained by Threat aggregator 150? Similarly, has there        been any suspicious Tier 3 activity?    -   Were any anomalous connections made within the datacenter during        the timeframe being investigated? As one example, suppose a        given user (“Frank”) typically enters a datacenter from a        particular IP address (or range of IP addresses), and then        connects to a first machine type (e.g., bastion), and then to a        second machine type (e.g., database_prod). If Frank has directly        connected to database_prod (instead of first going through        bastion) during the timeframe, this can be surfaced using the        insider graph.    -   Who is (the original user) responsible for running a particular        process?

An example of an insider behavior graph being used in an investigationis depicted in FIGS. 4C and 4D. FIG. 4C depicts a baseline of behaviorfor a user, “Bill.” As shown in FIG. 4C, Bill typically logs into adatacenter from the IP address, 71.198.44.40 (427). He typically makesuse of ssh (428), and sudo (429), makes use of a set of typicalapplications (430) and connects (as root) with the external service,api.lacework.net (431).

Suppose Bill's credentials are compromised by a nefarious outsider(“Eve”). FIG. 4D depicts an embodiment of how the graph depicted in FIG.4C would appear once Eve begins exfiltrating data from the datacenter.Eve logs into the datacenter (using Bill's credentials) from 52.5.66.8(432). As Bill, Eve escalates her privilege to root (e.g., via su), andthen becomes a different user, Alex (e.g., via su alex). As Alex, Eveexecutes a script, “sneak.sh” (433), which launches another script,“post.sh” (434), which contacts external server 435 which has an IPaddress of 52.5.66.7, and transmits data to it. Edges 436-439 eachrepresent changes in Bill's behavior. As previously mentioned, suchchanges can be detected as anomalies and associated alerts can begenerated. As a first example, Bill logging in from an IP address he hasnot previously logged in from (436) can generate an alert. As a secondexample, while Bill does typically make use of sudo (429), he has notpreviously executed sneak.sh (433) or post.sh (434) and the execution ofthose scripts can generate alerts as well. As a third example, Bill hasnot previously communicated with server 435, and an alert can begenerated when he does so (439). Considered individually, each of edges436-439 may indicate nefarious behavior, or may be benign. As an exampleof a benign edge, suppose Bill begins working from a home office twodays a week. The first time he logs in from his home office (i.e., froman IP address that is not 71.198.44.40), an alert can be generated thathe has logged in from a new location. Over time, however, as Billcontinues to log in from his home office but otherwise engages intypical activities, Bill's graph will evolve to include logins from both71.198.44.40 and his home office as baseline behavior. Similarly, ifBill begins using a new tool in his job, an alert can be generated thefirst time he executes the tool, but over time will become part of hisbaseline.

In some cases, a single edge can indicate a serious threat. For example,if server 432 (or 435) is included in a known bad IP listing, edge 436(or 439) indicates compromise. An alert that includes an appropriateseverity level (e.g., “threat level high”) can be generated. In othercases, a combination of edges could indicate a threat (where a singleedge might otherwise result in a lesser warning). In the example shownin FIG. 4D, the presence of multiple new edges is indicative of aserious threat. Of note, even though “sneak.sh” and “post.sh” wereexecuted by Alex, because data platform 12 also keeps track of anoriginal user, the compromise of user B's account will be discovered.

FIG. 4E illustrates a representation of an embodiment of a user logingraph. In the example of FIG. 4E, tier 0 (440) clusters source IPaddresses as belonging to a particular country (including an “unknown”country) or as a known bad IP. Tier 1 (441) clusters user logins, andtier 2 (442) clusters type of machine class into which a user is loggingin. The user login graph tracks the typical login behavior of users. Byinteracting with a representation of the graph, answers to questionssuch as the following can be obtained:

-   -   Where is a user logging in from?    -   Have any users logged in from a known bad address?    -   Have any non-developer users accessed development machines?    -   Which machines does a particular user access?

Examples of alerts that can be generated using the user login graphinclude:

-   -   A user logs in from a known bad IP address.    -   A user logs in from a new country for the first time.    -   A new user logs into the datacenter for the first time.    -   A user accesses a machine class that the user has not previously        accessed.

One way to track privilege changes in a datacenter is by monitoring aprocess hierarchy of processes. To help filter out noisycommands/processes such as “su -u,” the hierarchy of processes can beconstrained to those associated with network activity. In a *nix system,each process has two identifiers assigned to it, a process identifier(PID) and a parent process identifier (PPID). When such a system starts,the initial process is assigned a PID 0. Each user process has acorresponding parent process.

Using techniques described herein, a graph can be constructed (alsoreferred to herein as a privilege change graph) which models privilegechanges. In particular, a graph can be constructed which identifieswhere a process P1 launches a process P2, where P1 and P2 each have anassociated user U1 and U2, with U1 being an original user, and U2 beingan effective user. In the graph, each node is a cluster of processes(sharing a CType) executed by a particular (original) user. As all theprocesses in the cluster belong to the same user, a label that can beused for the cluster is the user's username. An edge in the graph, froma first node to a second node, indicates that a user of the first nodechanged its privilege to the user of the second node.

FIG. 4F illustrates an example of a privilege change graph. In theexample shown in FIG. 4F, each node (e.g., nodes 444 and 445) representsa user. Privilege changes are indicated by edges, such as edge 446.

As with other graphs, anomalies in graph 443 can be used to generatealerts. Three examples of such alerts are as follows:

-   -   New user entering the datacenter. Any time a new user enters the        datacenter and runs a process, the graph will show a new node,        with a new CType. This indicates a new user has been detected        within the datacenter. FIG. 4F is a representation of an example        of an interface that depicts such an alert. Specifically, as        indicated in region 447, an alert for the time period 1 pm-2 pm        on June 8 was generated. The alert identifies that a new user,        Bill (448) executed a process.    -   Privilege change. As explained above, a new edge, from a first        node (user A) to a second node (user B) indicates that user A        has changed privilege to user B.    -   Privilege escalation. Privilege escalation is a particular case        of privilege change, in which the first user becomes root.

An example of an anomalous privilege change and an example of ananomalous privilege escalation are each depicted in graph 450 of FIG.4G. In particular, as indicated in region 451, two alerts for the timeperiod 2 pm-3 pm on June 8 were generated (corresponding to thedetection of the two anomalous events). In region 452, root has changedprivilege to the user “daemon,” which root has not previously done. Thisanomaly is indicated to the user by highlighting the daemon node (e.g.,outlining it in a particular color, e.g., red). As indicated by edge453, Bill has escalated his privilege to the user root (which cansimilarly be highlighted in region 454). This action by Bill representsa privilege escalation.

An Extensible query interface for dynamic data compositions and filterapplications will now be described.

As described herein, datacenters are highly dynamic environments. And,different customers of data platform 12 (e.g., entity A vs. entity B)may have different/disparate needs/requirements of data platform 12,e.g., due to having different types of assets, different applications,etc. Further, as time progresses, new software tools will be developed,new types of anomalous behavior will be possible (and should bedetectable), etc. In various embodiments, data platform 12 makes use ofpredefined relational schema (including by having different predefinedrelational schema for different customers). However, the complexity andcost of maintaining/updating such predefined relational schema canrapidly become problematic—particularly where the schema includes a mixof relational, nested, and hierarchical (graph) datasets. In otherembodiments, the data models and filtering applications used by dataplatform 12 are extensible. As will be described in more detail below,in various embodiments, data platform 12 supports dynamic querygeneration by automatic discovery of join relations via static ordynamic filtering key specifications among composable data sets. Thisallows a user of data platform 12 to be agnostic to modifications madeto existing data sets as well as creation of new data sets. Theextensible query interface also provides a declarative and configurablespecification for optimizing internal data generation and derivations.

As will also be described in more detail below, data platform 12 isconfigured to dynamically translate user interactions (e.g., receivedvia web app 120) into SQL queries (and without the user needing to knowhow to write queries). Such queries can then be performed (e.g., byquery service 166) against any compatible backend (e.g., data store 30).

FIG. 4H illustrates an example of a user interacting with a portion ofan interface. When a user visits data platform 12 (e.g., via web app 120using a browser), data is extracted from data store 30 as needed (e.g.,by query service 166), to provide the user with information, such as thevisualizations depicted variously herein). As the user continues tointeract with such visualizations (e.g., clicking on graph nodes,entering text into search boxes, navigating between tabs (e.g., tab 455vs. 465)), such interactions act as triggers that cause query service166 to continue to obtain information from data store 30 as needed (andas described in more detail below).

In the example shown in FIG. 4H, user A is viewing a dashboard thatprovides various information about entity A users (455), during the timeperiod March 2 at midnight-March 25 at 7 pm (which she selected byinteracting with region 456). Various statistical information ispresented to user A in region 457. Region 458 presents a timeline ofevents that occurred during the selected time period. User A has optedto list only the critical, high, and medium events during the timeperiod by clicking on the associated boxes (459-461). A total of 55 lowseverity, and 155 info-only events also occurred during the time period.Each time user A interacts with an element in FIG. 4H (e.g., clicks onbox 461, clicks on link 464-1, or clicks on tab 465), her actions aretranslated/formalized into filters on the data set and used todynamically generate SQL queries. The SQL queries are generatedtransparently to user A (and also to a designer of the user interfaceshown in FIG. 4H).

User A notes in the timeline (462) that a user, Harish, connected to aknown bad server (examplebad.com) using wget, an event that has acritical severity level. User A can click on region 463 to expanddetails about the event inline (which will display, for example, thetext “External connection made to known bad host examplebad.com at port80 from application ‘wget’ running on host dev1.lacework.internal asuser harish”) directly below line 462. User A can also click on link464-1, which will take her to a dossier for the event (depicted in FIG.4I). As will be described in more detail below, a dossier is a templatefor a collection of visualizations.

As shown in interface 466, the event of Harish using wget to contactexamplebad.com on March 16 was assigned an event ID of 9291 by dataplatform 12 (467). For convenience to user A, the event is also added toher dashboard in region 476 as a bookmark (468). A summary of the eventis depicted in region 469. By interacting with boxes shown in region470, user A can see a timeline of related events. In this case, user Ahas indicated that she would like to see other events involving the wgetapplication (by clicking box 471). Events of critical and mediumsecurity involving wget occurred during the one hour window selected inregion 472.

Region 473 automatically provides user A with answers to questions thatmay be helpful to have answers to while investigating event 9291. Ifuser A clicks on any of the links in the event description (474), shewill be taken to a corresponding dossier for the link. As one example,suppose user A clicks on link 475. She will then be presented withinterface 477 shown in FIG. 4J.

Interface 477 is an embodiment of a dossier for a domain. In thisexample, the domain is “examplebad.com,” as shown in region 478. Supposeuser A would like to track down more information about interactionsentity A resources have made with examplebad.com between January 1 andMarch 20. She selects the appropriate time period in region 479 andinformation in the other portions of interface 477 automatically updateto provide various information corresponding to the selected time frame.As one example, user A can see that contact was made with examplebad.coma total of 17 times during the time period (480), as well as a list ofeach contact (481). Various statistical information is also included inthe dossier for the time period (482). If she scrolls down in interface477, user A will be able to view various polygraphs associated withexamplebad.com, such as an application-communication polygraph (483).

Data stored in data store 30 can be internally organized as an activitygraph. In the activity graph, nodes are also referred to as Entities.Activities generated by Entities are modeled as directional edgesbetween nodes. Thus, each edge is an activity between two Entities. Oneexample of an Activity is a “login” Activity, in which a user Entitylogs into a machine Entity (with a directed edge from the user to themachine). A second example of an Activity is a “launch” Activity, inwhich a parent process launches a child process (with a directed edgefrom the parent to the child). A third example of an Activity is a “DNSquery” Activity, in which either a process or a machine performs a query(with a directed edge from the requestor to the answer, e.g., an edgefrom a process to www.example.com). A fourth example of an Activity is anetwork “connected to” Activity, in which processes, IP addresses, andlisten ports can connect to each other (with a directed edge from theinitiator to the server).

As will be described in more detail below, query service 166 provideseither relational views or graph views on top of data stored in datastore 30. Typically, a user will want to see data filtered using theactivity graph. For example, if an entity was not involved in anactivity in a given time period, that entity should be filtered out ofquery results. Thus, a request to show “all machines” in a given timeframe will be interpreted as “show distinct machines that were active”during the time frame.

Query service 166 relies on three main data model elements: fields,entities, and filters. As used herein, a field is a collection of valueswith the same type (logical and physical). A field can be represented ina variety of ways, including: 1. a column of relations (table/view), 2.a return field from another entity, 3. an SQL aggregation (e.g., COUNT,SUM, etc.), 4. an SQL expression with the references of other fieldsspecified, and 5. a nested field of a JSON object. As viewed by queryservice 166, an entity is a collection of fields that describe a dataset. The data set can be composed in a variety of ways, including: 1. arelational table, 2. a parameterized SQL statement, 3. DynamicSQLcreated by a Java function, and 4. join/project/aggregate/subclass ofother entities. Some fields are common for all entities. One example ofsuch a field is a “first observed” timestamp (when first use of theentity was detected). A second example of such a field is the entityclassification type (e.g., one of: 1. Machine (on which an agent isinstalled), 2. Process, 3. Binary, 4. UID, 5. IP, 6. DNS Information, 7.ListenPort, and 8. PType). A third example of such a field is a “lastobserved” timestamp.

A filter is an operator that: 1. takes an entity and field values asinputs, 2. a valid SQL expression with specific reference(s) of entityfields, or 3. is a conjunct/disjunct of filters. As will be described inmore detail below, filters can be used to filter data in various ways,and limit data returned by query service 166 without changing theassociated data set.

As mentioned above, a dossier is a template for a collection ofvisualizations. Each visualization (e.g., the box including chart 484)has a corresponding card, which identifies particular target informationneeded (e.g., from data store 30) to generate the visualization. Invarious embodiments, data platform 12 maintains a global set ofdossiers/cards. Users of data platform 12 such as user A can build theirown dashboard interfaces using preexisting dossiers/cards as components,and/or they can make use of a default dashboard (which incorporatesvarious of such dossiers/cards).

A JSON file can be used to store multiple cards (e.g., as part of aquery service catalog). A particular card is represented by a singleJSON object with a unique name as a field name.

Each card may be described by the following named fields:

TYPE: the type of the card. Example values include:

-   -   Entity (the default type)    -   SQL    -   Filters    -   DynamicSQL    -   graphFilter    -   graph    -   Function    -   Template

PARAMETERS: a JSON array object that contains an array of parameterobjects with the following fields:

-   -   name (the name of the parameter)    -   required (a Boolean flag indicating whether the parameter is        required or not)    -   default (a default value of the parameter)    -   props (a generic JSON object for properties of the parameter.        Possible values are: “utype” (a user defined type), and “scope”        (an optional property to configure a namespace of the        parameter))    -   value (a value for the parameter—non-null to override the        default value defined in nested source entities)

SOURCES: a JSON array object explicitly specifying references of inputentities. Each source reference has the following attributes:

-   -   name (the card/entity name or fully-qualified Table name)    -   type (required for base Table entity)    -   alias (an alias to access this source entity in other fields        (e.g., returns, filters, groups, etc))

RETURNS: a required JSON array object of a return field object. A returnfield object can be described by the following attributes:

-   -   field (a valid field name from a source entity)    -   expr (a valid SQL scalar expression. References to input fields        of source entities are specified in the format of        #{Entity.Field}. Parameters can also be used in the expression        in the format of ${ParameterName})    -   type (the type of field, which is required for return fields        specified by expr. It is also required for all return fields of        an Entity with an SQL type)    -   alias (the unique alias for return field)    -   aggr (possible aggregations are: COUNT, COUNT_DISTINCT,        DISTINCT, MAX, MIN, AVG, SUM, FIRST_VALUE, LAST_VALUE)    -   case (JSON array object represents conditional expressions        “when” and “expr”)    -   fieldsFrom, and, except (specification for projections from a        source entity with excluded fields)    -   props (general JSON object for properties of the return field.        Possible properties include: “filterGroup,” “title,” “format,”        and “utype”)

PROPS: generic JSON objects for other entity properties

SQL: a JSON array of string literals for SQL statements. Each stringliteral can contain parameterized expressions ${ParameterName} and/orcomposable entity by #{EntityName}

GRAPH: required for graph entity. Has the following required fields:

-   -   source (including “type,” “props,” and “keys”)    -   target (including “type,” “props,” and “keys”)    -   edge (including “type” and “props”)

JOINS: a JSON array of join operators. Possible fields for a joinoperator include:

-   -   type (possible join types include: “loj”—Left Outer Join,        “join”—Inner Join, “in”—Semi Join, “implicit”—Implicit Join)    -   left (a left hand side field of join)    -   right (a right hand side field of join)    -   keys (key columns for multi-way joins)    -   order (a join order of multi-way joins)

FKEYS: a JSON array of FilterKey(s). The fields for a FilterKey are:

-   -   type (type of FilterKey)    -   fieldRefs (reference(s) to return fields of an entity defined in        the sources field)    -   alias (an alias of the FilterKey, used in implicit join        specification)

FILTERS: a JSON array of filters (conjunct). Possible fields for afilter include:

-   -   type (types of filters, including: “eq”—equivalent to SQL=,        “ne”—equivalent to SQL < >, “ge”—equivalent to SQL >=,        “gt”—equivalent to SQL >, “le”—equivalent to SQL <=,        “lt”-equivalent to SQL <, “like”—equivalent to SQL LIKE, “not        like”—equivalent to SQL NOT LIKE, “rlike”—equivalent to SQL        RLIKE (Snowflake specific), “not rlike”—equivalent to SQL NOT        RLIKE (Snowflake specific), “in”—equivalent to SQL IN, “not        in”—equivalent to SQL NOT IN)    -   expr (generic SQL expression)    -   field (field name)    -   value (single value)    -   values (for both IN and NOT IN)

ORDERS: a JSON array of ORDER BY for returning fields. Possibleattributes for the ORDER BY clause include:

-   -   field (field ordinal index (1 based) or field alias)    -   order (asc/desc, default is ascending order)

GROUPS: a JSON array of GROUP BY for returning fields. Field attributesare:

-   -   field (ordinal index (1 based) or alias from the return fields)

LIMIT: a limit for the number of records to be returned

OFFSET: an offset of starting position of returned data. Used incombination with limit for pagination.

Suppose customers of data platform 12 (e.g., entity A and entity B)request new data transformations or a new aggregation of data from anexisting data set (as well as a corresponding visualization for thenewly defined data set). As mentioned above, the data models andfiltering applications used by data platform 12 are extensible. Thus,two example scenarios of extensibility are (1) extending the filter dataset, and (2) extending a FilterKey in the filter data set.

Data platform 12 includes a query service catalog that enumerates cardsavailable to users of data platform 12. New cards can be included foruse in data platform 12 by being added to the query service catalog(e.g., by an operator of data platform 12). For reusability andmaintainability, a single external-facing card (e.g., available for usein a dossier) can be composed of multiple (nested) internal cards. Eachnewly added card (whether external or internal) will also haveassociated FilterKey(s) defined. A user interface (UI) developer canthen develop a visualization for the new data set in one or more dossiertemplates. The same external card can be used in multiple dossiertemplates, and a given external card can be used multiple times in thesame dossier (e.g., after customization). Examples of external cardcustomization include customization via parameters, ordering, and/orvarious mappings of external data fields (columns).

As mentioned above, a second extensibility scenario is one in which aFilterKey in the filter data set is extended (i.e., existing templatefunctions are used to define a new data set). As also mentioned above,data sets used by data platform 12 are composable/reusable/extensible,irrespective of whether the data sets are relational or graph data sets.One example data set is the User Tracking polygraph, which is generatedas a graph data set (comprising nodes and edges). Like other polygraphs,User Tracking is an external data set that can be visualized both as agraph (via the nodes and edges) and can also be used as a filter dataset for other cards, via the cluster identifier (CID) field.

As mentioned above, as users such as user A navigate through/interactwith interfaces provided by data platform 12 (e.g., as shown in FIG.4H), such interactions trigger query service 166 to generate and performqueries against data store 30. Dynamic composition of filter datasetscan be implemented using FilterKeys and FilterKey Types. A FilterKey canbe defined as a list of columns and/or fields in a nested structure(e.g., JSON). Instances of the same FilterKey Type can be formed as anImplicit Join Group. The same instance of a FilterKey can participate indifferent Implicit Join Groups. A list of relationships among allpossible Implicit Join Groups is represented as a Join graph for theentire search space to create a final data filter set by traversingedges and producing Join Path(s).

Each card (e.g., as stored in the query service catalog and used in adossier) can be introspected by a /card/describe/CardID REST request.

At runtime (e.g., whenever it receives a request from web app 120),query service 166 parses the list of implicit joins and creates a Joingraph to manifest relationships of FilterKeys among Entities. A Joingraph (an example of which is depicted in FIG. 4K) comprises a list ofJoin Link(s). A Join Link represents each implicit join group by thesame FilterKey type. A Join Link maintains a reverse map(Entity-to-FilterKey) of FilterKeys and their Entities. As previouslymentioned, Entities can have more than one FilterKey defined. Thereverse map guarantees one FilterKey per Entity can be used for eachJoinLink. Each JoinLink also maintains a list of entities for thepriority order of joins. Each JoinLink is also responsible for creatingand adding directional edge(s) to graphs. An edge represents a possiblejoin between two Entities.

At runtime, each Implicit Join uses the Join graph to find all possiblejoin paths. The search of possible join paths starts with the outerFilterKey of an implicit join. One approach is to use a shortest pathapproach, with breadth first traversal and subject to the followingcriteria:

-   -   Use the priority order list of Join Links for all entities in        the same implicit join group.    -   Stop when a node (Entity) is reached which has local filter(s).    -   Include all join paths at the same level (depth).    -   Exclude join paths based on the predefined rules (path of        edges).

FIG. 4L illustrates an example of a process for dynamically generatingand executing a query. In various embodiments, process 485 is performedby data platform 12. The process begins at 486 when a request isreceived to filter information associated with activities within anetwork environment. One example of such a request occurs in response touser A clicking on tab 465. Another example of such a request occurs inresponse to user A clicking on link 464-1. Yet another example of such arequest occurs in response to user A clicking on link 464-2 andselecting (e.g., from a dropdown) an option to filter (e.g., include,exclude) based on specific criteria that she provides (e.g., an IPaddress, a username, a range of criteria, etc.).

At 487, a query is generated based on an implicit join. One example ofprocessing that can be performed at 487 is as follows. As explainedabove, one way dynamic composition of filter datasets can be implementedis by using FilterKeys and FilterKey Types. And, instances of the sameFilterKey Type can be formed as an Implicit Join Group. A Join graph forthe entire search space can be constructed from a list of allrelationships among all possible Join Groups. And, a final data filterset can be created by traversing edges and producing one or more JoinPaths. Finally, the shortest path in the join paths is used to generatean SQL query string.

One approach to generating an SQL query string is to use a querybuilding library (authored in an appropriate language such as Java). Forexample, a common interface “sqlGen” may be used in conjunction withprocess 485 is as follows. First, a card/entity is composed by a list ofinput cards/entities, where each input card recursively is composed byits own list of input cards. This nested structure can be visualized asa tree of query blocks(SELECT) in standard SQL constructs. SQLgeneration can be performed as the traversal of the tree from root toleaf entities (top-down), calling the sqlGen of each entity. Each entitycan be treated as a subclass of the Java class(Entity). An implicit joinfilter (EntityFilter) is implemented as a subclass of Entity, similar tothe right hand side of a SQL semi-join operator. Unlike the static SQLsemi-join construct, it is conditionally and recursively generated evenif it is specified in the input sources of the JSON specification.Another recursive interface can also be used in conjunction with process485, preSQLGen, which is primarily the entry point for EntityFilter torun a search and generate nested implicit join filters. During preSQLGenrecursive invocations, the applicability of implicit join filters isexamined and pushed down to its input subquery list. Another top-downtraversal, pullUpCachable, can be used to pull up common sub-queryblocks, including those dynamically generated by preSQLGen, such thatSELECT statements of those cacheable blocks are generated only once attop-level WITH clauses. A recursive interface, sqlWith, is used togenerate nested subqueries inside WITH clauses. The recursive calls of asqlWith function can generate nested WITH clauses as well. An sqlFromfunction can be used to generate SQL FROM clauses by referencing thosesubquery blocks in the WITH clauses. It also produces INNER/OUTER joinoperators based on the joins in the specification. Another recursiveinterface, sqlWhere, can be used to generate conjuncts and disjuncts oflocal predicates and semi-join predicates based on implicit jointransformations. Further, sqlProject, sqlGroupBy, sqlOrderBy, andsqlLimitOffset can respectively be used to translate the correspondingdirectives in JSON spec to SQL SELECT list, GROUP BY, ORDER BY, andLIMIT/OFFSET clauses.

Returning to process 485, at 488, the query (generated at 487) is usedto respond to the request. As one example of the processing performed at488, the generated query is used to query data store 30 and provide(e.g., to web app 120) fact data formatted in accordance with a schema(e.g., as associated with a card associated with the request received at486).

Although the examples described herein largely relate to embodimentswhere data is collected from agents and ultimately stored in a datastore such as those provided by Snowflake, in other embodiments datathat is collected from agents and other sources may be stored indifferent ways. For example, data that is collected from agents andother sources may be stored in a data warehouse, data lake, data mart,and/or any other data store.

A data warehouse may be embodied as an analytic database (e.g., arelational database) that is created from two or more data sources. Sucha data warehouse may be leveraged to store historical data, often on thescale of petabytes. Data warehouses may have compute and memoryresources for running complicated queries and generating reports. Datawarehouses may be the data sources for business intelligence (‘BI’)systems, machine learning applications, and/or other applications. Byleveraging a data warehouse, data that has been copied into the datawarehouse may be indexed for good analytic query performance, withoutaffecting the write performance of a database (e.g., an OnlineTransaction Processing (‘OLTP’) database). Data warehouses also enablethe joining data from multiple sources for analysis. For example, asales OLTP application probably has no need to know about the weather atvarious sales locations, but sales predictions could take advantage ofthat data. By adding historical weather data to a data warehouse, itwould be possible to factor it into models of historical sales data.

Data lakes, which store files of data in their native format, may beconsidered as “schema on read” resources. As such, any application thatreads data from the lake may impose its own types and relationships onthe data. Data warehouses, on the other hand, are “schema on write,”meaning that data types, indexes, and relationships are imposed on thedata as it is stored in the EDW. “Schema on read” resources may bebeneficial for data that may be used in several contexts and poseslittle risk of losing data. “Schema on write” resources may bebeneficial for data that has a specific purpose, and good for data thatmust relate properly to data from other sources. Such data stores mayinclude data that is encrypted using homomorphic encryption, dataencrypted using privacy-preserving encryption, smart contracts,non-fungible tokens, decentralized finance, and other techniques.

Data marts may contain data oriented towards a specific business linewhereas data warehouses contain enterprise-wide data. Data marts may bedependent on a data warehouse, independent of the data warehouse (e.g.,drawn from an operational database or external source), or a hybrid ofthe two. In embodiments described herein, different types of data stores(including combinations thereof) may be leveraged. Such data stores maybe proprietary or may be embodied as vendor provided products orservices such as, for example, Google BigQuery, Druid, Amazon Redshift,IBM db2, Dremio, Databricks Lakehouse Platform, Cloudera, Azure SynapseAnalytics, and others.

The deployments (e.g., a customer's cloud deployment) that are analyzed,monitored, evaluated, or otherwise observed by the systems describedherein (e.g., systems that include components such as the platform 12 ofFIG. 1D, the data collection agents described herein, and/or othercomponents) may be provisioned, deployed, and/or managed usinginfrastructure as code (‘IaC’). IaC involves the managing and/orprovisioning of infrastructure through code instead of through manualprocesses. With IaC, configuration files may be created that includeinfrastructure specifications. IaC can be beneficial as configurationsmay be edited and distributed, while also ensuring that environments areprovisioned in a consistent manner. IaC approaches may be enabled in avariety of ways including, for example, using IaC software tools such asTerraform by HashiCorp. Through the usage of such tools, users maydefine and provide data center infrastructure using JavaScript ObjectNotation (‘JSON’), YAML, proprietary formats, or some other format. Insome embodiments, the configuration files may be used to emulate a clouddeployment for the purposes of analyzing the emulated cloud deploymentusing the systems described herein. Likewise, the configuration filesthemselves may be used as inputs to the systems described herein, suchthat the configuration files may be inspected to identifyvulnerabilities, misconfigurations, violations of regulatoryrequirements, or other issues. In fact, configuration files for multiplecloud deployments may even be used by the systems described herein toidentify best practices, to identify configuration files that deviatefrom typical configuration files, to identify configuration files withsimilarities to deployments that have been determined to be deficient insome way, or the configuration files may be leveraged in some other waysto detect vulnerabilities, misconfigurations, violations of regulatoryrequirements, or other issues prior to deploying an infrastructure thatis described in the configuration files. In some embodiments thetechniques described herein may be use in multi-cloud, multi-tenant,cross-cloud, cross-tenant, cross-user, industry cloud, digital platform,and other scenarios depending on specific need or situation.

In some embodiments, the deployments that are analyzed, monitored,evaluated, or otherwise observed by the systems described herein (e.g.,systems that include components such as the platform 12 of FIG. 1D, thedata collection agents described herein, and/or other components) may bemonitored to determine the extent to which a particular component hasexperienced “drift” relative to its associated IaC configuration.Discrepancies between how cloud resources were defined in an IaCconfiguration file and how they are currently configured in runtime maybe identified and remediation workflows may be initiated to generate analert, reconfigure the deployment, or take some other action. Suchdiscrepancies may occur for a variety of reasons. Such discrepancies mayoccur, for example, due to maintenance operations being performed, dueto incident response tasks being carried out, or for some other reason.Readers will appreciate that while IaC helps avoid initialmisconfigurations of a deployment by codifying and enforcing resourcecreation, resource configuration, security policies, and so on, thesystems described herein may prevent unwanted drift from occurringduring runtime and after a deployment has been created in accordancewith an IaC configuration.

In some embodiments, the deployments (e.g., a customer's clouddeployment) that are analyzed, monitored, evaluated, or otherwiseobserved by the systems described herein (e.g., systems that includecomponents such as the platform 12 of FIG. 1D, the data collectionagents described herein, and/or other components) may also beprovisioned, deployed, and/or managed using security as code (‘SaC’).SaC extends IaC concepts by defining cybersecurity policies and/orstandards programmatically, so that the policies and/or standards can bereferenced automatically in the configuration scripts used to provisioncloud deployments. Stated differently, SaC can automate policyimplementation and cloud deployments may even be compared with thepolicies to prevent “drift.” For example, if a policy is created whereall personally identifiable information (PIP) or personal healthinformation (PHI′) must be encrypted when it is stored, that policy istranslated into a process that is automatically launched whenever adeveloper submits code, and code that violates the policy may beautomatically rejected.

In some embodiments, SaC may be implemented by initially classifyingworkloads (e.g., by sensitivity, by criticality, by deployment model, bysegment). Policies that can be instantiated as code may subsequently bedesigned. For example, compute-related policies may be designed,access-related policies may be designed, application-related policiesmay be designed, network-related policies may be designed, data-relatedpolicies may be designed, and so on. Security as code may then beinstantiated through architecture and automation, as successfulimplementation of SaC can benefit from making key architectural-designdecisions and executing the right automation capabilities. Next,operating model protections may be built and supported. For example, anoperating model may “shift left” to maximize self-service and achievefull-life-cycle security automation (e.g., by standardizing commondevelopment toolchains, CI/CD pipelines, and the like). In such anexample, security policies and access controls may be part of thepipeline, automatic code review and bug/defect detection may beperformed, automated build processes may be performed, vulnerabilityscanning may be performed, checks against a risk-control framework maybe made, and other tasks may be performed all before deploying aninfrastructure or components thereof.

The systems described herein may be useful in analyzing, monitoring,evaluating, or otherwise observing a GitOps environment. In a GitOpsenvironment, Git may be viewed as the one and only source of truth. Assuch, GitOps may require that the desired state of infrastructure (e.g.,a customer's cloud deployment) be stored in version control such thatthe entire audit trail of changes to such infrastructure can be viewedor audited. In a GitOps environment, all changes to infrastructure areembodied as fully traceable commits that are associated with committerinformation, commit IDs, time stamps, and/or other information. In suchan embodiment, both an application and the infrastructure (e.g., acustomer's cloud deployment) that supports the execution of theapplication are therefore versioned artifacts and can be audited usingthe gold standards of software development and delivery. Readers willappreciate that while the systems described herein are described asanalyzing, monitoring, evaluating, or otherwise observing a GitOpsenvironment, in other embodiments other source control mechanisms may beutilized for creating infrastructure, making changes to infrastructure,and so on. In these embodiments, the systems described herein maysimilarly be used for analyzing, monitoring, evaluating, or otherwiseobserving such environments.

As described in other portions of the present disclosure, the systemsdescribed herein may be used to analyze, monitor, evaluate, or otherwiseobserve a customer's cloud deployment. While securing traditionaldatacenters requires managing and securing an IP-based perimeter withnetworks and firewalls, hardware security modules (‘HSMs’), securityinformation and event management (‘SIEM’) technologies, and otherphysical access restrictions, such solutions are not particularly usefulwhen applied to cloud deployments. As such, the systems described hereinmay be configured to interact with and even monitor other solutions thatare appropriate for cloud deployments such as, for example, “zero trust”solutions.

A zero trust security model (a.k.a., zero trust architecture) describesan approach to the design and implementation of IT systems. A primaryconcept behind zero trust is that devices should not be trusted bydefault, even if they are connected to a managed corporate network suchas the corporate LAN and even if they were previously verified. Zerotrust security models help prevent successful breaches by eliminatingthe concept of trust from an organization's network architecture. Zerotrust security models can include multiple forms of authentication andauthorization (e.g., machine authentication and authorization,human/user authentication and authorization) and can also be used tocontrol multiple types of accesses or interactions (e.g.,machine-to-machine access, human-to-machine access).

In some embodiments, the systems described herein may be configured tointeract with zero trust solutions in a variety of ways. For example,agents that collect input data for the systems described herein (orother components of such systems) may be configured to access variousmachines, applications, data sources, or other entity through a zerotrust solution, especially where local instances of the systemsdescribed herein are deployed at edge locations. Likewise, given thatzero trust solutions may be part of a customer's cloud deployment, thezero trust solution itself may be monitored to identify vulnerabilities,anomalies, and so on. For example, network traffic to and from the zerotrust solution may be analyzed, the zero trust solution may be monitoredto detect unusual interactions, log files generated by the zero trustsolution may be gathered and analyzed, and so on.

In some embodiments, the systems described herein may leverage varioustools and mechanisms in the process of performing its primary tasks(e.g., monitoring a cloud deployment). For example, Linux eBPF ismechanism for writing code to be executed in the Linux kernel space.Through the usage of eBPF, user mode processes can hook into specifictrace points in the kernel and access data structures and otherinformation. For example, eBPF may be used to gather information thatenables the systems described herein to attribute the utilization ofnetworking resources or network traffic to specific processes. This maybe useful in analyzing the behavior of a particular process, which maybe important for observability/SIEM.

The systems described may be configured to collect security event logs(or any other type of log or similar record of activity) and telemetryin real time for threat detection, for analyzing compliancerequirements, or for other purposes. In such embodiments, the systemsdescribed herein may analyze telemetry in real time (or near real time),as well as historical telemetry, to detect attacks or other activitiesof interest. The attacks or activities of interest may be analyzed todetermine their potential severity and impact on an organization. Infact, the attacks or activities of interest may be reported, andrelevant events, logs, or other information may be stored for subsequentexamination.

In one embodiment, systems described herein may be configured to collectsecurity event logs (or any other type of log or similar record ofactivity) and telemetry in real time to provide customers with a SIEM orSIEM-like solution. SIEM technology aggregates event data produced bysecurity devices, network infrastructure, systems, applications, orother source. Centralizing all of the data that may be generated by acloud deployment may be challenging for a traditional SIEM, however, aseach component in a cloud deployment may generate log data or otherforms of machine data, such that the collective amount of data that canbe used to monitor the cloud deployment can grow to be quite large. Atraditional SIEM architecture, where data is centralized and aggregated,can quickly result in large amounts of data that may be expensive tostore, process, retain, and so on. As such, SIEM technologies mayfrequently be implemented such that silos are created to separate thedata.

In some embodiments of the present disclosure, data that is ingested bythe systems described herein may be stored in a cloud-based datawarehouse such as those provided by Snowflake and others. Given thatcompanies like Snowflake offer data analytics and other services tooperate on data that is stored in their data warehouses, in someembodiments one or more of the components of the systems describedherein may be deployed in or near Snowflake as part of a secure datalake architecture (a.k.a., a security data lake architecture, a securitydata lake/warehouse). In such an embodiment, components of the systemsdescribed herein may be deployed in or near Snowflake to collect data,transform data, analyze data for the purposes of detecting threats orvulnerabilities, initiate remediation workflows, generate alerts, orperform any of the other functions that can be performed by the systemsdescribed herein. In such embodiments, data may be received from avariety of sources (e.g., EDR or EDR-like tools that handle endpointdata, cloud access security broker (‘CASB’) or CASB-like tools thathandle data describing interactions with cloud applications, Identityand Access Management (‘IAM’) or IAM-like tools, and many others),normalized for storage in a data warehouse, and such normalized data maybe used by the systems described herein. In fact, the systems describedherein may actually implement the data sources (e.g., an EDR tool, aCASB tool, an IAM tool) described above.

In some embodiments one data source that is ingested by the systemsdescribed herein is log data, although other forms of data such asnetwork telemetry data (flows and packets) and/or many other forms ofdata may also be utilized. In some embodiments, event data can becombined with contextual information about users, assets, threats,vulnerabilities, and so on, for the purposes of scoring, prioritizationand expediting investigations. In some embodiments, input data may benormalized, so that events, data, contextual information, or otherinformation from disparate sources can be analyzed more efficiently forspecific purposes (e.g., network security event monitoring, useractivity monitoring, compliance reporting). The embodiments describedhere offer real-time analysis of events for security monitoring,advanced analysis of user and entity behaviors, querying and long-rangeanalytics for historical analysis, other support for incidentinvestigation and management, reporting (for compliance requirements,for example), and other functionality.

In some embodiments, the systems described herein may be part of anapplication performance monitoring (‘APM’) solution. APM software andtools enable the observation of application behavior, observation of itsinfrastructure dependencies, observation of users and business keyperformance indicators (‘KPIs’) throughout the application's life cycle,and more. The applications being observed may be developed internally,as packaged applications, as software as a service (‘SaaS’), or embodiedin some other ways. In such embodiments, the systems described hereinmay provide one or more of the following capabilities:

-   -   The ability to operate as an analytics platform that ingests,        analyzes, and builds context from traces, metrics, logs, and        other sources.    -   Automated discovery and mapping of an application and its        infrastructure components.    -   Observation of an application's complete transactional behavior,        including interactions over a data communications network.    -   Monitoring of applications running on mobile (native and        browser) and desktop devices.    -   Identification of probable root causes of an application's        performance problems and their impact on business outcomes.    -   Integration capabilities with automation and service management        tools.    -   Analysis of business KPIs and user journeys (for example, login        to check-out).    -   Domain-agnostic analytics capabilities for integrating data from        third-party sources.    -   Endpoint monitoring to understand the user experience and its        impact on business outcomes.    -   Support for virtual desktop infrastructure (‘VDI’) monitoring.

In embodiments where the systems described herein are used for APM, somecomponents of the system may be modified, other components may be added,some components may be removed, and other components may remain thesame. In such an example, similar mechanisms as described elsewhere inthis disclosure may be used to collect information from theapplications, network resources used by the application, and so on. Thegraph based modelling techniques may also be leveraged to perform someof the functions mentioned above, or other functions as needed.

In some embodiments, the systems described herein may be part of asolution for developing and/or managing artificial intelligence (‘AI’)or machine learning (‘ML’) applications. For example, the systemsdescribed herein may be part of an AutoML tool that automate the tasksassociated with developing and deploying ML models. In such an example,the systems described herein may perform various functions as part of anAutoML tool such as, for example, monitoring the performance of a seriesof processes, microservices, and so on that are used to collectivelyform the AutoML tool. In other embodiments, the systems described hereinmay perform other functions as part of an AutoML tool or may be used tomonitor, analyze, or otherwise observe an environment that the AutoMLtool is deployed within.

In some embodiments, the systems described herein may be used to manage,analyze, or otherwise observe deployments that include other forms ofAI/ML tools. For example, the systems described herein may manage,analyze, or otherwise observe deployments that include AI services. AIservices are, like other resources in an as-a-service model, ready-mademodels and AI applications that are consumable as services and madeavailable through APIs. In such an example, rather than using their owndata to build and train models for common activities, organizations mayaccess pre-trained models that accomplish specific tasks. Whether anorganization needs natural language processing (NLP′), automatic speechrecognition (‘ASR’), image recognition, or some other capability, AIservices simply plug-and-play into an application through an API.Likewise, the systems described herein may be used to manage, analyze,or otherwise observe deployments that include other forms of AI/ML toolssuch as Amazon Sagemaker (or other cloud machine-learning platform thatenables developers to create, train, and deploy ML models) and relatedservices such as Data Wrangler (a service to accelerate data prep forML) and Pipelines (a CI/CD service for ML).

In some embodiments, the systems described herein may be used to manage,analyze, or otherwise observe deployments that include various dataservices. For example, data services may include secure data sharingservices, data marketplace services, private data exchanges services,and others. Secure data sharing services can allow access to live datafrom its original location, where those who are granted access to thedata simply reference the data in a controlled and secure manner,without latency or contention from concurrent users. Because changes todata are made to a single version, data remains up-to-date for allconsumers, which ensures data models are always using the latest versionof such data. Data marketplace services operate as a single location toaccess live, ready-to-query data (or data that is otherwise ready forsome other use). A data marketplace can even include a “feature stores,”which can allow data scientists to repurpose existing work. For example,once a data scientist has converted raw data into a metric (e.g., costsof goods sold), this universal metric can be found quickly and used byother data scientists for quick analysis against that data.

In some embodiments, the systems described herein may be used to manage,analyze, or otherwise observe deployments that include distributedtraining engines or similar mechanisms such as, for example, such astools built on Dask. Dask is an open source library for parallelcomputing that is written in Python. Dask is designed to enable datascientists to improve model accuracy faster, as Dask enables datascientists can do everything in Python end-to-end, which means that theyno longer need to convert their code to execute in environments likeApache Spark. The result is reduced complexity and increased efficiency.The systems described herein may also be used to manage, analyze, orotherwise observe deployments that include technologies such as RAPIDS(an open source Python framework which is built on top of Dask). RAPIDSoptimizes compute time and speed by providing data pipelines andexecuting data science code entirely on graphics processing units (GPUs)rather than CPUs. Multi-cluster, shared data architecture, DataFrames,Java user-defined functions (UDF) are supported to enable trained modelsto run within a data warehouse.

In some embodiments, the systems described herein may be leveraged forthe specific use case of detecting and/or remediating ransomware attacksand/or other malicious action taken with respect to data, systems,and/or other resources associated with one or more entities. Ransomwareis a type of malware from cryptovirology that threatens to publish thevictim's data or perpetually block access to such data unless a ransomis paid. In such embodiments, ransomware attacks may be carried out in amanner such that patterns (e.g., specific process-to-processcommunications, specific data access patterns, unusual amounts ofencryption/re-encryption activities) emerge, where the systems describedherein may monitor for such patterns. Alternatively, ransomware attacksmay involve behavior that deviates from normal behavior of a clouddeployment that is not experiencing a ransomware attack, such that themere presence of unusual activity may trigger the systems describedherein to generate alerts or take some other action, even withoutexplicit knowledge that the unusual activity is associated with aransomware attack.

In some embodiments, particular policies may be put in place the systemsdescribed herein may be configured to enforce such policies as part ofan effort to thwart ransomware attacks. For example, particular networksharing protocols (e.g., Common Internet File System (‘CIFS’), NetworkFile System (‘NFS’)) may be avoided when implementing storage for backupdata, policies that protect backup systems may be implemented andenforced to ensure that usable backups are always available, multifactorauthentication for particular accounts may be utilized and accounts maybe configured with the minimum privilege required to function, isolatedrecovery environments may be created and isolation may be monitored andenforced to ensure the integrity of the recovery environment, and so on.As described in the present disclosure, the systems described herein maybe configured to explicitly enforce such policies or may be configuredto detect unusual activity that represents a violation of such policies,such that the mere presence of unusual activity may trigger the systemsdescribed herein to generate alerts or take some other action, evenwithout explicit knowledge that the unusual activity is associated witha violation of a particular policy.

Readers will appreciate that ransomware attacks are often deployed aspart of a larger attack that may involve, for example:

-   -   Penetration of the network through means such as, for example,        stolen credentials and remote access malware.    -   Stealing of credentials for critical system accounts, including        subverting critical administrative accounts that control systems        such as backup, Active Directory (‘AD’), DNS, storage admin        consoles, and/or other key systems.    -   Attacks on a backup administration console to turn off or modify        backup jobs, change retention policies, or even provide a        roadmap to where sensitive application data is stored.    -   Data theft attacks.

As a result of the many aspects that are part of a ransomware attack,embodiments of the present disclosure may be configured as follows:

-   -   The systems may include one or more components that detect        malicious activity based on the behavior of a process.    -   The systems may include one or more components that store        indicator of compromise (‘IOC’) or indicator of attack (‘IOA’)        data for retrospective analysis.    -   The systems may include one or more components that detect and        block fileless malware attacks.    -   The systems may include one or more components that remove        malware automatically when detected.    -   The systems may include a cloud-based, SaaS-style, multitenant        infrastructure.    -   The systems may include one or more components that identify        changes made by malware and provide the recommended remediation        steps or a rollback capability.    -   The systems may include one or more components that detect        various application vulnerabilities and memory exploit        techniques.    -   The systems may include one or more components that continue to        collect suspicious event data even when a managed endpoint is        outside of an organization's network.    -   The systems may include one or more components that perform        static, on-demand malware detection scans of folders, drives,        devices, or other entities.    -   The systems may include data loss prevention (DLP)        functionality.

In some embodiments, the systems described herein may manage, analyze,or otherwise observe deployments that include deception technologies.Deception technologies allow for the use of decoys that may be generatedbased on scans of true network areas and data. Such decoys may bedeployed as mock networks running on the same infrastructure as the realnetworks, but when an intruder attempts to enter the real network, theyare directed to the false network and security is immediately notified.Such technologies may be useful for detecting and stopping various typesof cyber threats such as, for example, Advanced Persistent Threats(‘APTs’), malware, ransomware, credential dumping, lateral movement andmalicious insiders. To continue to outsmart increasingly sophisticatedattackers, these solutions may continuously deploy, support, refresh andrespond to deception alerts.

In some embodiments, the systems described herein may manage, analyze,or otherwise observe deployments that include various authenticationtechnologies, such as multi-factor authentication and role-basedauthentication. In fact, the authentication technologies may be includedin the set of resources that are managed, analyzed, or otherwiseobserved as interactions with the authentication technologies maymonitored. Likewise, log files or other information retained by theauthentication technologies may be gathered by one or more agents andused as input to the systems described herein.

In some embodiments, the systems described herein may be leveraged forthe specific use case of detecting supply chain attacks. Morespecifically, the systems described herein may be used to monitor adeployment that includes software components, virtualized hardwarecomponents, and other components of an organization's supply chain suchthat interactions with an outside partner or provider with access to anorganization's systems and data can be monitored. In such embodiments,supply chain attacks may be carried out in a manner such that patterns(e.g., specific interactions between internal and external systems)emerge, where the systems described herein may monitor for suchpatterns. Alternatively, supply chain attacks may involve behavior thatdeviates from normal behavior of a cloud deployment that is notexperiencing a supply chain attack, such that the mere presence ofunusual activity may trigger the systems described herein to generatealerts or take some other action, even without explicit knowledge thatthe unusual activity is associated with a supply chain attack.

In some embodiments, the systems described herein may be leveraged forother specific use cases such as, for example, detecting the presence of(or preventing infiltration from) cryptocurrency miners (e.g., bitcoinminers), token miners, hashing activity, non-fungible token activity,other viruses, other malware, and so on. As described in the presentdisclosure, the systems described herein may monitor for such threatsusing known patterns or by detecting unusual activity, such that themere presence of unusual activity may trigger the systems describedherein to generate alerts or take some other action, even withoutexplicit knowledge that the unusual activity is associated with aparticular type of threat, intrusion, vulnerability, and so on.

The systems described herein may also be leveraged for endpointprotection, such the systems described herein form all of or part of anendpoint protection platform. In such an embodiment, agents, sensors, orsimilar mechanisms may be deployed on or near managed endpoints such ascomputers, servers, virtualized hardware, internet of things (‘IotT’)devices, mobile devices, phones, tablets, watches, other personaldigital devices, storage devices, thumb drives, secure data storagecards, or some other entity. In such an example, the endpoint protectionplatform may provide functionality such as:

-   -   Prevention and protection against security threats including        malware that uses file-based and fileless exploits.    -   The ability to apply control (allow/block) to access of        software, scripts, processes, microservices, and so on.    -   The ability to detect and prevent threats using behavioral        analysis of device activity, application activity, user        activity, and/or other data.    -   The ability for facilities to investigate incidents further        and/or obtain guidance for remediation when exploits evade        protection controls    -   The ability to collect and report on inventory, configuration        and policy management of the endpoints.    -   The ability to manage and report on operating system security        control status for the monitored endpoints.    -   The ability to scan systems for vulnerabilities and        report/manage the installation of security patches.    -   The ability to report on internet, network and/or application        activity to derive additional indications of potentially        malicious activity.

Example embodiments are described in which policy enforcement, threatdetection, or some other function is carried out by the systemsdescribed herein by detecting unusual activity, such that the merepresence of unusual activity may trigger the systems described herein togenerate alerts or take some other action, even without explicitknowledge that the unusual activity is associated with a particular typeof threat, intrusion, vulnerability, and so on. Although these examplesare largely described in terms of identifying unusual activity, in theseexamples the systems described herein may be configured to learn whatconstitutes ‘normal activity’—where ‘normal activity’ is activityobserved, modeled, or otherwise identified in the absence of aparticular type of threat, intrusion, vulnerability, and so on. As such,detecting ‘unusual activity’ may alternatively be viewed as detecting adeviation from ‘normal activity’ such that ‘unusual activity’ does notneed to be identified and sought out. Instead, deviations from ‘normalactivity’ may be assumed to be ‘unusual activity’.

Readers will appreciate that while specific examples of thefunctionality that the systems described herein can provide are includedin the present disclosure, such examples are not to be interpreted aslimitations as to the functionality that the systems described hereincan provide. Other functionality may be provided by the systemsdescribed herein, all of which are within the scope of the presentdisclosure. For the purposes of illustration and not as a limitation,additional examples can include governance, risk, and compliance(‘GRC’), threat detection and incident response, identity and accessmanagement, network and infrastructure security, data protection andprivacy, identity and access management (‘IAM’), and many others.

In order to provide the functionality described above, the systemsdescribed herein or the deployments that are monitored by such systemsmay implement a variety of techniques. For example, the systemsdescribed herein or the deployments that are monitored by such systemsmay tag data and logs to provide meaning or context, persistentmonitoring techniques may be used to monitor a deployment at all timesand in real time, custom alerts may be generated based on rules, tags,and/or known baselines from one or more polygraphs, and so on.

Although examples are described above where data may be collected fromone or more agents, in some embodiments other methods and mechanisms forobtaining data may be utilized. For example, some embodiments mayutilize agentless deployments where no agent (or similar mechanism) isdeployed on one or more customer devices, deployed within a customer'scloud deployment, or deployed at another location that is external tothe data platform. In such embodiments, the data platform may acquiredata through one or more APIs such as the APIs that are availablethrough various cloud services. For example, one or more APIs thatenable a user to access data captured by Amazon CloudTrail may beutilized by the data platform to obtain data from a customer's clouddeployment without the use of an agent that is deployed on thecustomer's resources. In some embodiments, agents may be deployed aspart of a data acquisition service or tool that does not utilize acustomer's resources or environment. In some embodiments, agents(deployed on a customer's resources or elsewhere) and mechanisms in thedata platform that can be used to obtain data from through one or moreAPIs such as the APIs that are available through various cloud servicesmay be utilized. In some embodiments, one or more cloud servicesthemselves may be configured to push data to some entity (deployedanywhere), which may or may not be an agent. In some embodiments, otherdata acquisition techniques may be utilized, including combinations andvariations of the techniques described above, each of which is withinthe scope of the present disclosure.

Readers will appreciate that while specific examples of the clouddeployments that may be monitored, analyzed, or otherwise observed bythe systems described herein have been provided, such examples are notto be interpreted as limitations as to the types of deployments that maybe monitored, analyzed, or otherwise observed by the systems describedherein. Other deployments may be monitored, analyzed, or otherwiseobserved by the systems described herein, all of which are within thescope of the present disclosure. For the purposes of illustration andnot as a limitation, additional examples can include multi-clouddeployments, on-premises environments, hybrid cloud environments,sovereign cloud environments, heterogeneous environments, DevOpsenvironments, DevSecOps environments, GitOps environments, quantumcomputing environments, data fabrics, composable applications,composable networks, decentralized applications, and many others.

Readers will appreciate that while specific examples of the types ofdata that may be collected, transformed, stored, and/or analyzed by thesystems described herein have been provided, such examples are not to beinterpreted as limitations as to the types of data that may becollected, transformed, stored, and/or analyzed by the systems describedherein. Other types of data can include, for example, data collectedfrom different tools (e.g., DevOps tools, DevSecOps, GitOps tools),different forms of network data (e.g., routing data, network translationdata, message payload data, Wi-Fi data, Bluetooth data, personal areanetworking data, payment device data, near field communication data,metadata describing interactions carried out over a network, and manyothers), data describing processes executing in a container, lambda, EC2instance, virtual machine, or other execution environment), informationdescribing the execution environment itself, and many other types ofdata.

For further explanation, FIG. 5A sets forth a system for providing manyof the features described herein for user devices as a distributed edgeservice in accordance with some embodiments of the present disclosure.The system depicted in FIG. 5A includes a distributed edge platform 510.The distributed edge platform 510 may be similar to the systemsdescribed above where the distributed edge platform 510 can be used toperform tasks such as, for example, anomaly detection, threat detection,vulnerability detection, compliance monitoring, and many others. Thedistributed edge platform 510 may be deployed in a distributed fashion,such that instances of the distributed edge platform 510 are deployed ongeographically distributed execution environments, as will be describedin greater detail below.

The distributed edge platform 510 depicted in FIG. 5A may be utilized toprovide for continuous risk behavior based security to user devices.Such security is ‘continuous’ in the sense that data regarding theactivity of a user device is continuously gathered and evaluated inreal-time (or near real-time) rather than performing batch-basedevaluation. Such security is ‘behavior based’ in the sense that variousbehaviors of the user device are used as the primary inputs intosecurity evaluations. In many cases, such behaviors may not beconcerning on their own, but instead may represent a deviation fromtypical device activity that warrants additional investigation. Forexample, if the user device is connecting to a cloud deployment,creating EC2 instances, and executing software on those EC2 instances,those steps alone may not be concerning. If this happens at hours whenthe user is known to be asleep and the user has never logged into thecloud deployment nor deployed software on EC2 instances in the cloud,however, this behavior may be deemed to be suspicious.

The example depicted in FIG. 5A includes a private environment 502 thatis supporting a plurality of resources 504 a-n. The private environment502 may be embodied as a customer's datacenter, as a virtual privatecloud for a particular customer, as co-located resources, or in someother way. The private environment 502 may include a collection ofhardware resources, software resources, networking resources, and otherresources so that the private environment 502 can be used to provide anorganization or some other entity with an environment to execute theirsecure applications, store their secure data, and so on. In thisexample, the private environment 502 is ‘private’ in the sense that itis not available for public consumption. The private environment 502depicted in FIG. 5A is supporting a plurality of resources 504 a-n thatmay include, for example, software applications, databases, filesystems, various services (e.g., whether local SaaS offerings),development tools (e.g., automation servers, code repositories, etc. . .. ), and so on.

The private environment 502 also includes a connector 506 that can beused to connect the private environment to the distributed edge platform510 via a zero trust 508 authentication service. The connecter 506 maybe embodied, for example, as one or more modules of computer programinstructions executing on computer hardware, virtualized hardware, or insome other execution environment (including on resources in a dedicatednetworking components such as a router). The connector may be configuredto act as a communications interface between the private environment 502and the distributed edge platform 510. In such an example, accessbetween the private environment 502 and the distributed edge platform510 may involve the zero trust 508 authentication service, as anyparticipants in data communications between the private environment 502,the distributed edge platform 510, and the user devices 512, 514, 522that are monitored by the distributed edge platform 510 must beauthenticated by the zero trust 508 authentication service. The zerotrust 508 authentication service may be embodied, for example, as athird party authentication service such as Okta, or in some other way.In fact, readers will appreciate that the distributed edge platform 510may be integrated with (and may leverage) a variety of third party toolssuch as identity and access management tools, Mobile device management(‘MDM’) tools, and so on.

In the example depicted in FIG. 5A, a user device 512 can access theresources 504 a-n in the private environment 502 via the zero trust 508authentication service. In such an example, because all access occursthrough the zero trust 508 authentication service, the user device 512need not connect to a VPN, go through a firewall, or use a similarmechanism in order to securely access the resources 504 a-n in theprivate environment 502. In fact, the distributed edge platform 510 mayimplement policies that identify which users may access which resources504 a-n, what privileges each user has, and other policies so that theuser device 512 is only routed to (and given access to) a particularresource 504 a-n in the private environment 502 if the policies allowfor such access. The distributed edge platform 510 can do all sorts ofbehavior analysis (i.e., analysis of device activity and user activityas described below) to add in anomaly detection capabilities, threatassessment, risk assessment, and other security related capabilities asdescribed elsewhere in the present disclosure.

The example depicted in FIG. 5A also includes a SaaS environment 518that is supporting a plurality of resources 520 a-n. The SaaSenvironment 518 may be embodied, for example, as a public cloud that isaccessible using a particular account, as an environment provided by thevendor of some SaaS offering, or in some other way. The SaaS environment518 may include a collection of hardware resources, software resources,networking resources, and other resources so that the SaaS environment518 can be used to provide a vendor of software that is consumedas-a-service to offer their SaaS products. The SaaS offerings caninclude any software offered as-a-service including, for example,Salesforce, Office365, and many others. In this example, the resources520 a-n that may include software applications, databases, file systems,or anything else offered as-a-service.

In the example depicted in FIG. 5A, a user device 514 can access theresources 520 a-n in the SaaS environment 518 via a SaaS security 516module. The SaaS security 516 module may be embodied, for example, as alogin service or similar service that is implemented by a cloudcomputing environment or service vendor to control access to one or moreSaaS offerings. In such a way, access to the SaaS environment 518 mayonly occur in accordance with the requirements put in place by the cloudcomputing environment, the service vendor, or similar entity thatcontrols access to one or more SaaS offerings.

The example depicted in FIG. 5A also includes a public internet 528 thatis supporting a plurality of resources 526 a-n. The public internet 528may include a collection of hardware resources, software resources,networking resources, and other resources that can be used to offerresources 526 a-n such as websites, social media platforms, and anythingelse publicly accessible via a web browser, mobile application, or otherappropriate interface.

In the example depicted in FIG. 5A, a user device 522 can access theresources 526 a-n in the public internet 528 via a network security 524module. The network security 524 module may be embodied, for example, asone or more modules of computer program instructions that are configuredto perform tasks such as SSL inspection, DNS inspection, and other tasksdescribed in the present disclosure. In such an example, the networksecurity module may be configured to protect the user device 522 frommalware intrusions, computer viruses, or other threats that canoriginate from the public internet 528.

The distributed edge platform 510 may implement policies that identifywhich users may access which resources 520 a-n, what privileges eachuser has, and other policies so that the user device 514 is only able toaccess various resources if the policies allow for such access. In fact,the distributed edge platform 510 can do all sorts of behavior analysis(i.e., analysis of device activity and user activity as described below)to add in anomaly detection capabilities, threat assessment, riskassessment, and other security related capabilities as describedelsewhere in the present disclosure.

The distributed edge platform 510 depicted in FIG. 5A includes areal-time visibility and policy enforcement 530 module. The real-timevisibility and policy enforcement 530 module may be embodied, forexample, as one or more modules of computer program instructionsexecuting on computer hardware, virtualized hardware, or in some otherexecution environment. The real-time visibility and policy enforcement530 module may be configured to carry out many of the steps describedabove such as, for example, monitoring user activity (e.g., via datacommunications involving a user device or in some other way) to enforcevarious policies describing how the user devices may be utilized, whatresources the user devices may access, what privileges the user devicehas, and so on. The real-time visibility and policy enforcement 530module may also enable an administrator to have visibility into theactivities associated with the monitored user devices. The real-timevisibility and policy enforcement 530 module may be used to populate amonitoring interface used by the administrator, the real-time visibilityand policy enforcement 530 module may enable the administrator to querythe distributed edge platform 510 for information related to useractivity or device activity, and so on.

The distributed edge platform 510 depicted in FIG. 5A also includes auser behavior anomaly detection 532 module. The user behavior anomalydetection 532 module may be embodied, for example, as one or moremodules of computer program instructions executing on computer hardware,virtualized hardware, or in some other execution environment. The userbehavior anomaly detection 532 may be configured to analyze userbehavior, user device activity, or other information to detect anomalousbehavior as described in greater detail elsewhere in the presentdisclosure.

The distributed edge platform 510 depicted in FIG. 5A also includes anevents, workflows, and auto management 534 module. The events,workflows, and auto management 534 module may be embodied, for example,as one or more modules of computer program instructions executing oncomputer hardware, virtualized hardware, or in some other executionenvironment. The events, workflows, and auto management 534 module maybe configured to generate alerts, initiate a remediation workflow (orsome other workflow), or perform other automatic remediation tasks asdescribed in greater detail elsewhere in the present disclosure.

For further explanation, FIG. 5B sets forth a system for providing manyof the features described herein for user devices as a distributed edgeservice in accordance with some embodiments of the present disclosure.The example depicted in FIG. 5B illustrates the distributed nature ofthe distributed edge service. The example depicted in FIG. 5Billustrates instances of the distributed edge platform 510 a, 510 b, 510c, 510 d that are executing at distinct population centers 540 a, 540 b,540 c, 540 d. Each of the population centers 540 a, 540 b, 540 c, 540 dmay be embodied, for example, as a geographically distributed executionenvironment such as a distinct availability zone that is provided by acloud services provider such as AWS, GCP, Azure, or others. In fact, thegeographically distributed execution environments (represented here aspopulation centers 540 a, 540 b, 540 c, 540 d) may even be provided bymultiple cloud services providers (e.g., population center 540 a issupported by AWS and population center 540 b is supported by GCP).

Readers will appreciate that each instance of the distributed edgeplatform 510 a, 510 b, 510 c, 510 d may, in some embodiments, bedeployed on a set of appliances that are deployed in various locations.Each appliance may include, for example, one or more servers or othercomputing devices, one or more networking devices, one or more storagedevices, and so on. In such an example, each appliance may be configuredto execute a particular instance of the distributed edge platform 510 a,510 b, 510 c, 510 d.

In the example depicted in FIG. 5B, the instance of the distributed edgeplatform 510 a, 510 b, 510 c, 510 d that is accessed by a particularuser device 512, 514, 522, 542 may be dependent upon the relativeproximity between each population center 540 a, 540 b, 540 c, 540 d andeach user device 512, 514, 522, 542. For example, each user device 512,514, 522, 542 may connect to the instance of the distributed edgeplatform 510 a, 510 b, 510 c, 510 d that is supported by the populationcenter 540 a, 540 b, 540 c, 540 d that is most physically proximate tothe user device 512, 514, 522, 542. The various instances of thedistributed edge platform 510 a, 510 b, 510 c, 510 d may be updated in acoordinated fashion and may share access to the same information suchthat each instance operates in the same manner as any other instance,even if two instances are deployed in different ways (e.g., on differentunderlying resources).

For further explanation, FIG. 6 sets forth a flow chart illustrating anexample method of detecting deviations from typical user behavior inaccordance with some embodiments of the present disclosure. As will bedescribed in greater detail below, detecting deviations from typicaluser behavior can be carried out with respect to end user devices. Forexample, detecting deviations from typical user behavior can includedetecting typical user behavior as reflected by the manner in which aparticular device (e.g., a laptop, a smartphone) is typically used andidentifying situations in which the particular device is being used in amanner that deviates from the typical usage pattern. Although notexpressly illustrated in FIG. 6 , the methods described in FIG. 6 andelsewhere in the present disclosure may be carried out by one or moremodules of computer program instructions executing on computer hardware,virtualized computer hardware, containers, or in some other executionenvironment.

The example method depicted in FIG. 6 includes identifying 602 alocation of a device that is associated with a user. The device that isassociated with a user may be embodied, for example, as a smartphone, asa tablet computer, as a laptop computer, or as some other device. Thedevice may be associated with the user because the user is logged intothe device, the device has been designated for use by the user (e.g.,the device is a company issued laptop provided by the user's employer),or the device is otherwise associated with the user. The location of thedevice may be embodied, for example, as a city and state (e.g., LosAngeles, CA), as a label of a known location (e.g., ‘home’), ordesignated in some other way.

Identifying 602 a location of a device that is associated with a usermay be carried out in a variety of ways. In fact, identifying 602 alocation of the device may be carried out using any of multiplemechanisms for determining a device's location and (in some embodiments)verifying that each mechanism yields the same outcome, or at least thesame outcome within a predetermined threshold. For example, identifying602 a location of the device may be carried out by identifying wirelessnetworks or wireless access points that the device can connect to anddetermining the location of the available wireless networks or wirelessaccess points. Once the locations of wireless networks or wirelessaccess points that the device can connect to have been identified, othermechanisms may be used to verify the location of the device. Forexample, the source IP address of a data communications packet generatedby the device may be used to determine the location of the device. Insuch an example, if the locations of wireless networks or wirelessaccess points that the device can connect to and the location of thesource IP address of a data communications packet generated by thedevice match each other, the matching locations may be identified 602 asbeing the location of a device. The mechanisms for determining adevice's location will be explained in greater detail below.

The example method depicted in FIG. 6 also includes determining 604device activity associated with the user. The device activity associatedwith the user can include information describing the usage of thedevice. The device activity can include, for example, informationdescribing applications that are being executed or utilized on thedevice, information describing how those applications are beingutilized, information describing files being accessed via the device,information describing data sources being accessed via the device,information describing data communications coming into and flowing outof the device, and many others. In general, the device activity can beused to ascertain how a device is being used, when the device is beingused, where the device is being used, or any other quantifiable aspectof device usage.

Determining 604 device activity associated with the user may be carriedout, for example, by one or more data collection agents that areexecuting on the device, by one or more data collection agents thatexecuting at some other location off of the device, or in some otherway. That is, while some information describing device activity may comefrom the device (OS version, what apps are running locally, etc.), theinformation describing device activity may also come from outside thedevice. For example, other cloud services that report what users aredoing may be leveraged (e.g., activity logs from Office 365 may be used,activity logs from Dropbox may be used). In such an example, even if noagents or other data collection programs were deployed on the deviceitself, information describing device activity may still be obtained. Assuch, determining 604 device activity associated with the user may becarried out by local agents executing on the monitored device itself, byagents executing elsewhere, or by combinations and variations thereof.The data collection agents (whether deployed on the user's device orelsewhere) may include, for example, one or more programs that can carryout a stream-based capture of network traffic in and out of the device(i.e., stream-oriented traffic processing), one or more programs thatcapture the usage of a device interface (e.g., a keystroke recorder, arecording program for capturing data acquired via a microphone), orother programs. In such a way, the agents may capture various aspectsdescribing how the device is being used, when it is being used, by whomit is being used, where the device is located when being used, and soon.

The example method depicted in FIG. 6 also includes determining 606,based on a profile associated with the user, that the device activityassociated with the user deviates from normal activity for the user. Theprofile associated with the user may include information describingnormal activity for the user. The normal activity for the user may bedetermined, for example, based on historical usage of the device (orsome other device) associated with the user. That is, normal activitymay be learned through an analysis of how the device has historicallybeen used rather than being specified exclusively as a set of rules. Thenormal activity may include, for example, an identification ofapplications on the device that are accessed by the user, the times thatthose applications are accessed, the locations from which thoseapplications are accessed, the order in which the applications on thedevice are typically accessed by the user, and so on. The particularmechanics of creating a user profile and learning what is normalactivity for the device will be described in greater detail below.

In the example method depicted in FIG. 6 , determining 606, based on aprofile associated with the user, that the device activity associatedwith the user deviates from normal activity for the user may be carriedout by comparing the device activity with the profile associated withthe user. A comparison between the device activity and the profileassociated with the user may reveal that device activity does not alignwith the profile associated with the user, which may be treated as adetection of abnormal activity.

Readers will appreciate that comparisons the device activity and theprofile associated with the user may utilize ranges, thresholds, orsimilar concepts to allow for minor deviations between the deviceactivity and the profile associated with the user. For example, if theprofile associated with the user indicates that the device is typicallylocated at the user's office between the hours of 9 AM and 5 PM onweekdays, but the device activity reveals that the user is still at theoffice at 6 PM on a particular Tuesday evening, this minor deviation maybe tolerated and may not rise to the level of triggering an alarm. Incontrast, if the device activity reveals that the user is at the officeat 2:30 AM on a Sunday morning, this may be viewed as being a largerdeviation. In this example, a threshold may be utilized such that theuser deviating from their normal activity by 90 minutes (as a verysimplified example used for ease of explanation) does not rise to thelevel of abnormal activity that would trigger an alarm or cause someother remediation workflow to be initiated. In other embodiments, othermechanisms may be used to allow for minor deviations between the deviceactivity and the profile associated with the user, where such minordeviations do not result in determining 606 that the device activityassociated with the user deviates from normal activity for the user.

For further explanation, FIG. 7 sets forth a flow chart illustrating anadditional example method of detecting deviations from typical userbehavior in accordance with some embodiments of the present disclosure.The example method depicted in FIG. 7 is similar to the example methoddepicted in FIG. 6 , as the example method depicted in FIG. 7 alsoincludes identifying 602 a location of a device that is associated witha user, determining 604 device activity associated with the user, anddetermining 606 that the device activity associated with the userdeviates from normal activity for the user.

The example method depicted in FIG. 7 also includes generating 702 theprofile associated with the user. Generating 702 the profile associatedwith the user may be carried out, for example, by utilizing datagathered by the agents described above as input to one or more machinelearning algorithms. In such a way, patterns may be identified andcorrelations may be detected that represent the normal activity of theuser. Such machine learning algorithms may detect, for example, that aparticular application is normally only used at certain times and fromcertain locations. For example, the data gathered by the agentsdescribed above may reveal that a VPN client on the device is typicallyonly used during traditional business hours (e.g., 8 AM-6 PM) onweekdays and when the device is located at a location other than theuser's office (as the private network may be directly accessible whenthe device is being used at the user's office). Readers will appreciatethat in other embodiments, the profile associated with the user may begenerated 702 in some other way. In such an example, the profileassociated with the user may be expressed as a trained machine learningmodel that may be deployed to differentiate between normal activity andabnormal activity associated with a user (or a user device).

In the example method depicted in FIG. 7 , determining 604 deviceactivity associated with the user can include identifying 704 one ormore applications being accessed on the device. Identifying 704 one ormore applications being accessed on the device may be carried out, forexample, by an agent that is executing on the device. For example, theagent may identify all running processes on the device and map (by theagent or by some other entity) the running processes to particularapplications, the agent may identify all binaries that are loaded on thedevice and map (by the agent or by some other entity) the binaries toparticular applications, the agent may query a device managementapplication, or the agent may identify 704 one or more applicationsaccessed by the device in some other way. In other embodiments,especially those in which the applications are SaaS offerings that areexecuted externally to the device (e.g., in a cloud environment),identifying 704 one or more applications being accessed on the devicemay be carried out in other ways such as, for example, by examiningactivity logs generated by the creator of the SaaS offering, bymonitoring the device for calls to the application, through the use ofan agent that monitors traffic in and out of the SaaS offering, or insome other way.

In the example method depicted in FIG. 7 , determining 604 deviceactivity associated with the user can also include identifying 706application behavior for the one or more applications. Identifying 706application behavior for the one or more applications can include, forexample, identifying the data communications endpoints that theapplications have communicated with, identifying data that has beenaccessed by the applications, identifying users or accounts that haveutilized the applications, identifying the time that the application wasused, and so on. Such application behavior can include any quantifiableaspect describing how the application was used, when the application wasused, by whom the application was used, and so on.

The example method depicted in FIG. 7 also includes, responsive todetermining that the device activity associated with the user deviatesfrom normal activity for the user, generating 708 an alert. The alertthat is generated 708 may include contextual information including thespecific actions (e.g., accessing a VPN client on a during non-businesshours on a weekend from a previously unknown location) that caused thealert to be generated. In such an example, the alert may be issued tothe user of the device, issued to a system administrator or similarentity that oversees a company's deployment, or issued elsewhere. Infact, the alert may be presented as part of a user-specific polygraph aswill be described in greater detail herein.

For further explanation, FIG. 8 sets forth a flow chart illustrating anadditional example method of detecting deviations from typical userbehavior in accordance with some embodiments of the present disclosure.The example method depicted in FIG. 8 is similar to the examples methoddepicted in FIGS. 6 and 7 , as the example method depicted in FIG. 8also includes identifying 602 a location of a device that is associatedwith a user, determining 604 device activity associated with the user,and determining 606 that the device activity associated with the userdeviates from normal activity for the user.

The example method depicted in FIG. 8 also includes generating 802 auser-specific polygraph. The user-specific polygraph may be generated802 in a manner that is similar to the manner in which polygraphs arecreated as described above. Although the user-specific polygraphs may besimilar to the polygraphs described above, the user-specific polygraphmay be distinct by virtue of each entity in the user-specific polygraphbeing specific to a particular user or user device. As one example of auser specific polygraph, FIGS. 15A and 15B include examples of auser-specific polygraph. As is depicted in greater detail below, theuser-specific polygraph can include the geographic location of thedevice and the device activity associated with the user, among otherpossible entities. In some embodiments, the user-specific polygraph canalso include one or more alerts, as described in greater detail below.

In the examples described herein, the device activity associated withthe user may be continuously monitored for deviation from normalactivity. The device activity may be continuously monitored fordeviation from normal activity, for example, by determining whetherdevice activity associated with the user deviates from normal activityfor the user (as specified in a user profile) every time that a changeto the device activity associated with the user occurs, as part of aprocess that is always executing on some computing resources, or in someother way. In such a way, the systems described herein may be providereal-time or near real-time detection of a deviation from normalactivity—rather than batch processing or other form of delayedprocessing of device activity to determine whether such activitydeviates from normal activity.

In the examples described herein, the device activity associated withthe user and the profile associated with the user include temporalinformation. The temporal information may be embodied, for example, asspecific dates or times when some activity occurred or normally occurs,as relative times when some activity occurred or normally occurs, rangesof times when some activity occurred or normally occurs, as durations oftime when some activity occurred or normally occurs, and so on. As such,the time at which some activity occurred may factor into an evaluationas to whether the activity represents normal activity.

For further explanation, FIG. 9 sets forth a flow chart illustrating anexample method of establishing a location profile for a user device inaccordance with some embodiments of the present disclosure. The examplemethod depicted in FIG. 9 includes gathering 902 information associatedwith the location of a user device. Gathering 902 information associatedwith the location of a user device may be carried out not only bygathering geolocation information, but also gathering information thatmay be useful in determining the nature of a particular location. Forexample, information may be gathered 902 to determine whether thelocation of the user device is a location where the user device isfrequently utilized, information may be gathered 902 to determinewhether the location of the user device is a location where utilizingthe user device to perform certain functions is allowed or prohibited,and so on. As such, the information associated with the location of auser device may be gathered 902 not just for the purposes of determiningwhere the device would be located on a map, but to use the location ofthe device as an input to determining whether a user or a user device isexhibiting anomalous behavior.

Readers will appreciate that many forms of information associated withthe location of a user device may gathered 902 through a variety ofmechanisms. Gathering 902 information associated with the location of auser device may be carried out, for example, using location-relatedcapabilities of the user device such as a global positioning system(‘GPS’) receiver, an Assisted GPS (‘AGPS’) chip, and so on. In otherembodiments, gathering 902 information associated with the location of auser device may be carried out using data communications relatedcapabilities of the user device. For example, a Wi-Fi adapter in theuser device may detect nearby networks and the Service Set Identifier(‘SSID’) associated with a detected network may be mapped to ageolocation through the use of tools such as Google Geolocation API.Likewise, examining data communications traffic sent by the user deviceand extracting the client IP address that is associated with the datacommunications traffic may be used in gathering 902 informationassociated with the location of the user device. In such an example, theclient IP address may be used to query one or more of a variety ofservices that convert IP addresses to geolocation information (e.g., acity/state, latitude and longitude, and so on). In fact, otherembodiments could leverage image sensors or other capabilities of theuser device to gather 902 information that may be informative for thepurposes of identify a user device's location.

Readers will appreciate that because the information associated with thelocation of a user device may be gathered 902 for reasons that extendwell beyond the context of a geolocation, the information associatedwith the location of a user device may be information that is moreuseful in describing the nature of a location rather than an absolutephysical location. For example, the particular set of device types thatthe user device can communicate with may be indicative of a location.Consider an example in which the user device can detect the presence ofa thermostat, a smart refrigerator, multiple smart TVs, a router, andthe entertainment system of an automobile via its Wi-Fi adapter orBluetooth adapter. In such an example, this combination of reachabledevice types may be taken as an indication that the user device is at aprivate residence. In an example where the user device can only detectthe presence of other personal communications devices and also detectthe presence of a wireless network that includes a phrase such as“free,” “public,” or “starbucks” in its network name, this combinationof reachable devices may be taken as an indication that the user deviceis at a public location (perhaps a coffee shop). In these examples,rather than attempting to determine a geolocation, information may begathered 902 that can be used to determine a relative location, a typeof location, characteristics of a location, or some other informationthat may be associated with a particular location.

The example method depicted in FIG. 9 also includes determining 904,based on the information associated with the location of a user device,whether the user device is being accessed at a known location. A ‘known’location, as the term is used here, can refer to a location that isassociated with an expected profile of device utilization based onpreviously observing how the user device (or some other device such as asimilar device, a device that is associated with similar users, and soon) is utilized at the ‘known’ location. For example, a user device maybe expected to be used in one way when the user device is located at theuser's home, the user device may be expected to be used in another waywhen the user device is located at the user's office, the user devicemay be expected to be used in yet another way when the user device islocated in the user's automobile, and so on. As such, in someembodiments a location may only be determined 904 to be a ‘knownlocation’ if the location has an associated set of user behaviors thatwould be expected to be observed when the user device is located at the‘known location’.

Readers will appreciate that a location is not necessarily ‘known’ inthe sense that the device's geolocation is known, although in someembodiments a known location may include a specific geolocation (e.g.,123 Avenue A, San Francisco, CA). For example, while it may not bepossible to determine the mailing address or street address where theuser device is located, the detected presence of a particular set ofother devices may be sufficient to determine that the user device is ata ‘home’ location, even if the exact street address of the home cannotbe determined. Likewise, detecting that the user device is located at aparticular set of GPS coordinates may be insufficient for determiningthat the location of the user device is ‘known’ if the set of GPScoordinates has no known relationship to a location where the userdevice has previously been used or is otherwise associated with a set ofbehaviors that would be expected to be observed when the user device isat the GPS coordinates. In fact, in some embodiments, the location ofthe user device may only be determined to be a ‘known’ location if theuser device has previously been accessed at the location or at someother location where utilization of the user device would be expected tobe similar. For example, if the location of the user device is at ahotel, this location may be determined to be a ‘known’ location byvirtue of the fact that the user device has been used in the past atother hotels (even if the user device has not been previously used atthe exact hotel that it is now located at).

The example method depicted in FIG. 9 also includes, responsive toaffirmatively 906 determining that the user device is being accessed ata known location, determining 908 a characterization of the knownlocation. A ‘characterization’ of the known location may be embodied asdescription of the location that can be associated with some set ofbehaviors that would be expected from the user device or the user byvirtue of the user device being located at a location that ischaracterized in a certain way. Consider an example in which thecharacterization is a ‘home’ location. In this example, a set ofexpected behaviors may be associated with the user or the user device byvirtue of the user device being at home. For example, it may be expectedthat the user device accesses streaming media services (e.g., Netflix)while the user device is at home, it may be expected that the userdevice accesses the public internet while the user device is at home,and so on. Alternatively, consider an example in which thecharacterization is a ‘work’ location. In this example, a set ofexpected behaviors may be associated with the user or the user device byvirtue of the user device being at home. For example, it may be expectedthat the user device accesses their employer's internal bill paymentsystems, it may be expected that the user device accesses theiremployer's code repository and internal networks while at work, and soon.

In the example depicted in FIG. 9 , various ‘characterizations’ oflocations may be generated through the use of one or more machinelearning models. Machine learning models may be used, for example, todetect clusters (e.g., clusters of devices that can be reached viarelatively short distance communications adapters in the devices) thatcan be identified as being at least a logical location. Such machinelearning models may take data gathered by one or more devices toidentify clusters that correspond to some location. For example, ifinformation is gathered indicating that a collection of user devices arelocated in a relatively small area and that a large percentage of thosedevices are connected to an internal corporate network for company ABC,the machine learning models may learn that the locations each device(and possibly some corresponding area surrounding each device) can becharacterized as being an office for company ABC. Readers willappreciate that other ‘characterizations’ may be generated using avariety of information, machine learning techniques, other labellingtechniques, or generated in some other way (e.g., by asking the user ofthe device or some administrator of the device to characterize theircurrent location through some interface).

Determining 908 a characterization of the known location may be carried,for example, through the use of a table, database, or some other datastructure/repository that associates known locations withcharacterizations of each known location. Such a repository can beconstructed over time by monitoring the behavior of the user device andother devices. As part of a process of generating characterizations ofvarious locations, the activity of the user device (and other devices)may also be monitored to learn what behavior constitutes ‘normal’behavior for the user or user device in various locations, usingtechniques such as those described herein.

The example method depicted in FIG. 9 also includes determining 910,based on the characterization of the known location, whether deviceutilization is anomalous. Determining 910 whether device utilization isanomalous may be carried out as described above, generally bydetermining the extent to which device activity is consistent withnormal activity that would be expected to be observed when the userdevice is at a known location that is characterized in a particular way.Readers will appreciate that a determination so to whether deviceutilization is anomalous may only be based in part on thecharacterization of the location, as other information may also be takeninto consideration (e.g., information from other devices, specificdetails around how the device is being used, policies that restrict howa device should be utilized at certain locations, etc. . . . ).

The example method depicted in FIG. 9 also includes, responsive todetermining that the user device is not 912 being accessed at a knownlocation, determining 914 a characterization of the unknown location.Determining 914 a characterization of the unknown location may becarried out, for example, by performing some workflow to attempt todetermine whether the unknown location is similar to a knowncharacterization. For example, one characterization for variouslocations may be an ‘in transit to work’ (or something similar)characterization that is generally associated with locations between auser's home location and a user's work location, so long as there issome indication that the user device is moving at an appropriate ratefrom the user's home location and the user's work location. In such anexample, if the workflow determines that the user device was located atthe user's home 10 minutes ago, a route between the user's home and theuser's office takes 20 minutes to traverse, and the user device iscurrently located near a midpoint of that route, the unknown locationmay be characterized as being ‘in transit to work’ or something similar.Likewise, an unknown location may be characterized in accordance withvarious environmental attributes detected by the user device. Forexample, if the user device is at an unknown public location with noWi-Fi networks that the user device is configured to access but the userdevice can access a cellular network, the unknown location may beconsistent with a known characterization of a device being at a ‘publiclocation without secure Wi-Fi access,’ such that the user device can becharacterized as being at a ‘public location without secure Wi-Fiaccess.’

In these examples, readers will appreciate that a library or catalog ofknown characterizations may be created by observing many devices overtime. In fact, each entry (i.e., each known characterization of alocation) in the catalog may associated with location information thattends to be associated with a particular characterization. For example,devices may be monitored to determine that when a device is placed in asilent mode between the hours of 8 AM-1 PM on a Sunday, and a touchscreen display for the device is not being interacted with by the user,and a microphone in the device detects periods of singing, the device isat a location that is characterized as a ‘religious services’ location.As such, information that was gathered 902 and is associated with anunknown location may be compared to signatures for the entries in thecatalog of characterizations to determine whether the unknown locationshould be characterized using one of the entries in the catalog.

Readers will appreciate that the characterization of an unknown locationmay be associated with a set of expected device behaviors. For example,when a user device is characterized as being ‘in transit to work’, itmay be expected that utilization of the user device may be extremelylimited as the user may be operating a vehicle. Likewise, when a userdevice is characterized as being at a ‘public location without secureWi-Fi access’, it may be expected that the user device will not be usedto access sensitive financial data or initiate substantial monetarytransactions on behalf of the user's employer.

The example method depicted in FIG. 9 also includes determining 916,based on the characterization of the unknown location, whether deviceutilization is anomalous. Determining 916 whether device utilization isanomalous may be carried out described above, generally by determiningthe extent to which device activity is consistent with normal activitythat would be expected to occur when the user device is at an unknownlocation that is characterized in a particular way. Readers willappreciate that a determination so to whether device utilization isanomalous may only be based in part on the characterization of thelocation, as other information may also be taken into consideration(e.g., information from other devices, specific details around how thedevice is being used, policies that restrict how a device should beutilized at certain locations, etc. . . . ).

For further explanation, FIG. 10 sets forth a flow chart illustrating anadditional example method of establishing a location profile for a userdevice in accordance with some embodiments of the present disclosure.The example method depicted in FIG. 10 is similar to the example methoddepicted in FIG. 9 , as the example depicted in FIG. 10 also includesgathering 902 information associated with the location of a user device,determining 904 whether the user device is being accessed at a knownlocation, determining 908 a characterization of a location, anddetermining 910 whether device utilization is anomalous.

In the example method depicted in FIG. 10 , determining 904 whether theuser device is being accessed at a known location can includedetermining 1002 whether the information associated with the location ofa user device matches, at least within a predetermined threshold,location information associated with one or more location profiles. Alocation profile may be embodied, for example, as a data structure thatassociates one or more known locations with information associated withthe known locations. The information that is associated with the knownlocations can include, for example, one or more device behaviors thatare typically encountered when a device is at the known location, ageographic boundary that defines the known location, one or morecharacterizations of the known location, temporal aspects of device anduser behavior that are typically observed at the known location, and soon. As such, the location profile may be used to correlate suchinformation with a known location, so that the location profile can beused to determine whether a particular device is at the known location.

In the example method depicted in FIG. 10 , the one or more locationprofiles may be created based on activity associated with the userdevice. For example, a location profile that is associated with the userdevice being at a hotel, on an airplane, at a coffee shop, at home, atwork, or at some other location may be based on monitored or observeddevice activity or user activity associated with the user device itself.Alternatively, the one or more location profiles may be created based onactivity associated with other devices. Such other devices may beembodied as, for example, similar devices for users that are part of thesame organization (e.g., the same company, the same department within acompany), devices for users that are determined to perform similar roles(e.g., engineers, accountants, legal team members) within anorganization, and so on. In such a way, a particular user device may bemonitored with the benefit of knowledge gleaned from monitoring otherdevices, which may be particularly useful when a particular user deviceis new and does not have a long history of monitored behavior.

In some embodiments, determining 910, 914 whether device utilization isanomalous may be further based on a temporal profile of device activity.The temporal profile of device activity may include, for example,information describing days and times that particular applications areused on the user device, information describing days and times that theuser device is typically being accessed or not being accessed,information describing the relative timing of different user or deviceactivities (e.g., updated code is committed from the user's device to acode repository after text editing software or some other software usedto write code has been utilized), or any other information thatassociates user behavior or device behavior with times at which suchbehavior is common, permitted, prohibited, and so on. The temporalprofile may be embodied as a database, table, or some other datastructure. In such embodiments, the relationship between times anddevice or user activities may be identified through the usage of one ormore machine learning models that take activities (and the timesassociated with the activities) as inputs to the machine learningmodels.

For further explanation, FIG. 11 sets forth a flow chart illustrating anadditional example method of establishing a location profile for a userdevice in accordance with some embodiments of the present disclosure.The example method depicted in FIG. 11 is similar to the example methodsdepicted in FIG. 9 and FIG. 10 , as the example depicted in FIG. 11 alsoincludes gathering 902 information associated with the location of auser device, determining 904 whether the user device is being accessedat a known location, determining 908 a characterization of a location,and determining 910 whether device utilization is anomalous based on thecharacterization of the known location. The example method depicted inFIG. 11 also includes creating 1102 a polygraph associated with the userdevice. Creating 1102 a polygraph associated with the user device may becarried out as described elsewhere in the present disclosure. Examplesof such device-specific polygraphs are included herein.

In the example depicted in FIG. 11 , determining 904 whether the userdevice is being accessed at a known location can include determining1104, based on one or more devices that are physically proximate to theuser device, a location of the user device. Readers will appreciate thatthe exact distance between the user device and the one or more otherdevices (e.g., one or more routers that emit a Wi-Fi signal) may not beneeded in order to determine 1104 a location of the user device. Theterm ‘physically proximate’ that is used here can be a threshold valuethat may even be a function of the particular wireless communicationsprotocol that is being used. For example, if the user device can detectthe presence of another device using a Bluetooth adapter, the userdevice may be assumed to be within 20 feet of the other device. If theuser device can detect the presence of another device using a Wi-Fiadapter, however, the user device may be assumed to be within 100 feetof the other device. Readers will appreciate that these distance andtechnologies are included only as examples.

Consider an example in which a user device is embodied as a laptopcomputer and the laptop computer can detect the presence of fivedistinct networks using its Wi-Fi adapter. In such an example, upondetecting the presence of each network, resources may be accessed thatassociate the access ID of the network with a physical locationassociated with the network. For example, Google maintains a databasethat can be accessed via various APIs to determine the latitude andlongitude associated with a wireless network's access ID. In someexamples, this database may be queried with the access ID of thedetected network to receive latitude and longitude informationassociated with the network. Readers will appreciate that, over time,the results obtained from querying such a database may be cached suchand reused. For example, if location information for each of the fivedistinct networks is obtained and the same user turns on a smartphonethat detects the same five networks, the cached results of thepreviously executed queries may be utilized to determine the firstgeolocation of the smartphone. Cached results may similarly be used forother users whose devices detect the same networks.

In the example depicted in FIG. 11 , determining 904 whether the userdevice is being accessed at a known location can include determining1106, based on data communications involving the user device, a locationof the user device. Determining 1106 a location of the user device basedon data communications involving the user device may be carried out, forexample, by examining data communications traffic sent by the device andextracting the client IP address that is associated with the datacommunications traffic. The client IP address may be used, for example,to query one or more of a variety of services that convert IP addressesto location information (e.g., a city/state, latitude and longitude, andso on). In an alternative embodiment, the IP address of the user devicemay be identified in other ways other than inspecting network traffic(e.g., through the use of CLI commands such as ipconfig).

Readers will appreciate that the location of a user device may bemisidentified, for example, if the user device is connected to a virtualprivate network or if the user device is being operated in some otherway where its IP address (or other data communications attribute) wouldnot be an accurate representation of the device's location. As such, insome embodiments multiple pieces of information that are associated witha location can be used rather than relying exclusively on a single pieceof information when determining a user device's location.

In some embodiments, a location cache may be maintained. As describedabove, as part of the process of determining a location of the userdevice, external resources may be accessed. For example, resources suchas those maintained by Google may be accessed that associate the accessID of a network with a physical location associated with the network.Such resources, however, may not be free to access, may be timely toaccess, the resource may return location information in a less thanideal format (e.g., the resource return latitude and longitudeinformation when the desired format of the location is a zip code), ormay be undesirable to access for other reasons. As such, the resultsobtained from querying such resources may be cached and reused in alocal location cache.

Such a location cache may be a data repository such as a database, afile, a table, or embodied in some other way. In some embodiments, thelocation cache may be ‘local’ in the sense that it is stored on the userdevice itself or stored in some location that does not suffer from thesame undesirable characteristics as the original resource. Continuingwith the example where a Google database may be accessed (for a monetarycharge) that associate the access ID of a network with a physicallocation associated with the network, after that initial access thelocation information associated with the access ID of the network may bemaintained in a database that the user device can access free of charge.Readers will appreciate that in these examples, the content contained inthe local location cache may be stored in the format desired by the userdevice (e.g., stored as city/state information rather than GPScoordinates).

Readers will appreciate that some embodiments may include a differentcombination of the steps described above, including variations of suchsteps. For example, some embodiments of establishing a location profilefor a user device may include gathering information associated with alocation of a user device, determining a characterization of thelocation, and determining, based on a characterization of the knownlocation, whether device utilization is anomalous such that the step ofdetermining whether a location is known is optional.

For further explanation, FIG. 12 sets forth a flow chart illustrating anexample method of detecting deviation from normal behavior of a userdevice in accordance with some embodiments of the present disclosure.

The example method depicted in FIG. 12 includes generating 1202, usinginformation describing historical activity associated with a userdevice, a trained model for detecting normal activity for the userdevice. The trained model may be embodied, for example, as a modelartifact that is created by a training process in which machine learningalgorithms are provided with training data to learn from. The trainedmodel, once deployed, can make predictions, identify patterns, andperform other functions on new data (i.e., data that was not part of thetraining data). Generating 1202 a trained model for detecting normalactivity for the user device using information describing historicalactivity associated with a user device may therefore be carried out, forexample, by applying one or more machine learning algorithms to atraining dataset that includes the information describing the historicalactivity associated with the user device. The information describing thehistorical activity associated with the user device can include, forexample, information describing the locations at which the user devicewas utilized at some point in the past, information describing theapplications on the user device that were executed at some point in thepast, information describing the dates and times the applications on theuser device that were executed, and so on. In such an example, activityassociated with the user device may be deemed to be ‘historical’ if theactivity occurred at some point before training occurred.

Consider an example in which training occurred at time to. In such anexample, any activity associated with the user device that occurredprior to time to would be ‘historical’ activity associated with the userdevice. Further assume that additional training occurred at time t₁,which is later than time to. In this example, activity associated withthe user device that occurred prior to time t₁ would be ‘historical’activity associated with the user device, at least with respect to theadditional training (whereas activity that occurred between time to andtime t₁ would not be ‘historical’ activity with respect to the originaltraining that occurred at time to).

The example method depicted in FIG. 12 also includes gathering 1204information describing current activity associated with the user device.The information current activity associated with the user device may beembodied, for example, as information describing activity associatedwith the user device that was gathered 1204 at a point in time such thatit was not available for inclusion in the set of training data that wasused to generate (or further refine via additional training) the trainedmodel. The information describing current activity of the user devicemay include the types of information described above (e.g., location ofthe user device, applications executed by the user device, and muchmore). The information describing the current activity of the userdevice may be gathered 1204, for example, by the agents described aboveincluding agents that are executing on the user device itself.

The example method depicted in FIG. 12 also includes determining 1206,by using the information describing current activity associated with theuser device as input to the trained model, whether the user device hasdeviated from normal activity. Determining 1206 whether the user devicehas deviated from normal activity by using the information describingcurrent activity associated with the user device as input to the trainedmodel may be carried out, for example, by executing the trained model onthe user device and coupling the trained model to a data stream, datarepository, or other source for the information describing currentactivity associated with the user device. In such an example, suchinformation may describe ‘current’ activity in the sense that theinformation represents activity that has occurred in some recent periodof time (e.g., the last minute, the last second), activity that is beingmonitored in real-time, activity that has occurred since the model wasmost recently trained, or activity that is ‘current’ as determined bysome other rule or heuristic.

For further explanation, FIG. 13 sets forth a flow chart illustrating anexample method of sets forth a flow chart illustrating an example methodof detecting deviation from normal behavior of a user device inaccordance with some embodiments of the present disclosure. The examplemethod depicted in FIG. 13 is similar to the example method depicted inFIG. 12 , as FIG. 13 also includes generating 1202 a trained model fordetecting normal activity for the user device, gathering 1204information describing current activity associated with the user device,and determining 1206 whether the user device has deviated from normalactivity.

In the example method depicted in FIG. 13 , generating 1202 a trainedmodel for detecting normal activity for the user device can includegenerating 1302 the trained model using information describing physicallocations at which the user device was utilized. As described above, thephysical locations at which the user device was utilized may bedetermined from a combination of sources (e.g., the location ofphysically proximate devices, data communications characteristics of theuser device, user input, location-detecting devices that are embeddedwithin the user device). Such locations at which the user device wasutilized may be included in a training dataset that is used to generatethe trained model. In fact, the trained model may ultimately identifydensity clusters such that locations that are not identical matches aredetermined to be the same location. For example, a cluster of locationsat which the user device was utilized may be identified as locationswithin the user's office complex, such that the user being on thenortheast side of the office building and the user being on thesouthwest side of the office building are not treated as distinctlocations, but are instead treated as the user being within the samedensity area that represents a single logical location. Likewise, acluster of locations that represent a commonly traversed path may betreated as a single entity. For example, the trained model may learn auser's daily route to work and may treat all of the individual locationsalong that route as a single entity. In some embodiments, particularlocations may be labelled (e.g., home, office, etc. . . . ) as part ofthe training process or after the model has been trained.

In the example method depicted in FIG. 13 , generating 1202 a trainedmodel for detecting normal activity for the user device can includegenerating 1304 the trained model using information describing usage ofone or more applications executed on the user device. The informationdescribing usage of one or more applications executed on the user devicemay include, for example, information describing when the applicationswere used, information describing what features of the applications wereused, information describing what external data sources were accessedwhen the applications were being used, information describing what datacommunications occurred when the applications were being used, and soon. Such information may be included in a training dataset that is usedto generate the trained model. In fact, training the model using thetraining datasets may reveal relationships between differentapplications, patterns related to how the user utilizes theapplications, actions that trigger the usage of the application (e.g.,the user typically accesses an email application after receiving anotification), and many other patterns that represent normal activity.As such, the trained model may be configured to identify deviations fromnormal activity.

In the example method depicted in FIG. 13 , generating 1202 a trainedmodel for detecting normal activity for the user device can includegenerating 1306 the trained model using information describing times atwhich activity previously occurred. The information describing times atwhich activity previously occurred may be embodied, for example, asabsolute values (e.g., at 12:36 PM on a specific date), as relativevalues (e.g., after using application X, within 30 minutes after turningon the user device), as a value that is expressed in its relationship tosome other activity or detected condition (e.g., while the user was intransit to work, while the user was at work), or in some other way. Suchinformation may be included in a training dataset that is used togenerate the trained model. In fact, training the model using thetraining datasets may reveal temporal relationships between differentactivities, patterns related to an ordering of a set of activities ordetected conditions, and many other patterns that represent normalactivity. As such, the trained model may be configured to identifydeviations from normal activity.

Readers will appreciate that while the embodiments described above arelargely described as individual types of information (e.g., locationinformation, temporal information, device usage information) that may beused to generate 1202 a trained model for detecting normal activity forthe user device, readers will appreciate that the process of generating1202 a trained model for detecting normal activity for the user devicemay actually include one or more machine learning algorithms ingestingcombinations of these types of information. In fact, other types ofinformation may also be used in the process of generating 1202 a trainedmodel for detecting normal activity for the user device. Readers willappreciate that any information that can be captured by an agent that isexecuting on the user device, including combinations thereof, can beused when generating 1202 a trained model for detecting normal activityfor the user device.

In the example method depicted in FIG. 13 , gathering 1204 informationdescribing current activity associated with the user device can includegathering 1308 information describing a physical location at which theuser device is currently being used. The information describing aphysical location at which the user device is currently being used maybe gathered 1308, for example, by one or more agents that are executingon the user device, by one or more agents that are executing on otherdevices (e.g., on a device that communicates with the user device over alocal network). Such information may include, for example, informationthat itself does not represent a location of the user device but whichmay be used to determine the location of the user device as describedabove. Such information may include information describing other devicesor network that are detected by the user device, information describingdata communications characteristics of the device, and so on. In otherembodiments, the information itself may represent a location of the userdevice. For example, such information may include global positioningsystem (‘GPS’) coordinates obtained from a GPS receiver in the userdevice.

In the example method depicted in FIG. 13 , gathering 1204 informationdescribing current activity associated with the user device can includegathering 1310 information describing usage of applications that arebeing accessed by the user device. The information describing usage ofapplications that are being accessed by the user device may be gathered1310, for example, by one or more agents that are executing on the userdevice, by one or more agents that are executing on a device/servicethat executes the application, or in other ways described above. Suchinformation may include, for example, information describing whichapplications are being executed, information describing what datacommunications networks are being accessed when executing anapplication, information describing particular features of theapplication that are being utilized, and many others.

In the example method depicted in FIG. 13 , gathering 1204 informationdescribing current activity associated with the user device can includegathering 1312 information describing times at which current activityoccurred on the device. The information describing times at whichcurrent activity occurred on the device may be gathered 1312, forexample, by one or more agents that are executing on the user device, byone or more agents that are executing elsewhere, by inspecting activitylogs, or in some other way. Such information may include, for example,information describing the exact time that some activity occurred, arange of times during which the activity occurred, a relative timeduring which the activity occurred, and so on.

For further explanation, FIG. 14 sets forth a flow chart illustrating anexample method of sets forth a flow chart illustrating an example methodof detecting deviation from normal behavior of a user device inaccordance with some embodiments of the present disclosure. The examplemethod depicted in FIG. 14 is similar to the example method depicted inFIGS. 12 and 13 , as FIG. 14 also includes generating 1202 a trainedmodel for detecting normal activity for the user device, gathering 1204information describing current activity associated with the user device,and determining 1206 whether the user device has deviated from normalactivity.

The example method depicted in FIG. 14 also includes periodically 1402retraining the trained model. Periodically 1402 retraining the trainedmodel may be carried out, for example, by including recently acquireddata describing various aspects of the user device's operation and usageinto a training data that is used to train the model. Such data may be‘recently acquired’ in the sense that it was not included in trainingdata that was previously used to train the model. The recently acquireddata may be used either alone or in combination with training data thatwas previously used to train the model as part of a process toretraining the trained model, at which point the retrained model may bedeployed on the user device. Readers will appreciate that retraining thetrained model periodically such as, for example, according to apredetermined schedule, upon the satisfaction of some condition (e.g., athreshold amount of new data has been acquired, sufficient resources tocarry out the retraining have become available, alerts are beinggenerated at a threshold level indicating that perhaps the trained modeldoes not sufficiently understand normal behavior), upon request from auser or administrator, or in some other way. Through such periodically1402 retraining the trained model, the trained model may be an evolvingentity that can differentiate between normal and abnormal activity evenas a user's interactions with the device or usage of the device changeover time.

The example method depicted in FIG. 14 also includes generating 1404 analert after determining that the user device has deviated from normalactivity. The alert may be generated 1404 and expressed through one ormore of the polygraphs described above. In other embodiments, alerts maybe delivered in some other way (e.g., as a notification that is sent tosome predetermined recipient using some predetermined deliverymechanism).

The example method depicted in FIG. 14 also includes initiating 1406 aremediation workflow after determining that the user device has deviatedfrom normal activity. Initiating 1406 a remediation workflow may becarried out as part of an auto-remediation capability that may beprovided to the monitored devices. The remediation workflow may beconfigured to perform a variety of tasks including, for example,restricting the access of the user device to certain data, restrictingthe usage of certain applications on the user device, enabling somefeature on the user device (e.g., communications over an unsecurednetwork is detected so a data encryption feature for data communicationsis enabled), or performing some other function. In some embodiments, theremediation workflow may be designed to either prevent the abnormalactivity from occurring or even enabling the abnormal activity upon thesatisfaction of some condition (e.g., an administrator approves theactivity, the user authenticates their identity).

For further explanation, FIG. 15A sets forth an example of auser-specific polygraph 1500 in accordance with some embodiments of thepresent disclosure. The user-specific polygraph 1500 depicted in FIG.15A may be generated in a similar manner as the polygraphs describedabove and may have similar features and capabilities. The user-specificpolygraph 1500 depicted in FIG. 15A includes a representation of a userand information describing the user, in this case designated as User ABC1502. The representation of the user may include, for example, a user'sname, a user's handle, or any other information associated with theuser. Such information associated with the user may be visible in theoriginal presentation of the polygraph or may be accessible in otherways (e.g., hovering a mouse over the user icon).

The user-specific polygraph 1500 depicted in FIG. 15A also includesinformation describing the location of the user (as represented by thelocation of the device that is being used by the user), depicted here asSan Francisco, CA 1504. The location may be obtained as described ingreater detail above. The representation of the location of the user mayinclude, for example, an identification of a city, geographicalcoordinates, an identification of a known location (e.g., home, office),or any other location information associated with the user. Suchlocation information associated with the user may be visible in theoriginal presentation of the polygraph or may be accessible in otherways (e.g., hovering a mouse over the user icon).

The user-specific polygraph 1500 depicted in FIG. 15A also includesinformation describing the user device, depicted here as user deviceMRD1 1506. The information describing the user device may include, forexample, a device name, a label for the device (e.g., phone, laptop,work laptop) or any other information describing the device. Such deviceinformation may be visible in the original presentation of the polygraphor may be accessible in other ways (e.g., hovering a mouse over the usericon).

The user-specific polygraph 1500 depicted in FIG. 15A also includesinformation describing applications accessed by the device, depictedhere as internal sales application 1508, web browser application 1510,ad messaging application 1512. Information describing the applicationsthat are accessed by the user device may be obtained as described ingreater detail above. The representation of the applications mayinclude, for example, a name of the application, a name of the binary, acustom label for the application, or any other information associatedwith the application including information describing the usage of theapplication. Such application-related information may be visible in theoriginal presentation of the polygraph or may be accessible in otherways (e.g., hovering a mouse over the user icon).

The user-specific polygraph 1500 depicted in FIG. 15A also includesinformation describing specific details regarding how a particularapplication is being used. For example, the internal sales application1508 is depicted as connecting to an internal sales database 1514.Information associated with the internal sales database (e.g., where thedatabase is hosted, what credentials were used to access the database,how many queries have been directed to the database) may also bedepicted or may be accessible in other ways (e.g., hovering a mouse overthe user icon). Likewise, the web browser application 1510 is depictedas being connected to different endpoints, which may be carried outthrough the usage of different tabs or different instances of the webbrowser application 1510. For example, the web browser application 1510is depicted as accessing a social media 1514 site, a productivity 1518site (e.g., Salesforce, a web-based repository), a bandwidth intensive1520 site such as a streaming video site, and a site that requiresprivate information 1522 such as a banking site. In some embodiments,additional information associated with each endpoint 1516, 1518, 1520,1522 may be displayed or may be accessible in other ways (e.g., hoveringa mouse over the user icon). In fact, the links between different iconsin polygraph may also be enriched with data. For example, the linksbetween the web browser application 1510 and the endpoints 1516, 1518,1520, 1522 may include information describing how long the connectionhas been established, how much data has been transferred since theconnection was established, and so on. In other embodiments, other dataassociated with the links between two icons may also be enriched withdata that may be visible in the default view of the polygraph oraccessible in some other way as described above.

For further explanation, FIG. 15B sets forth an example of auser-specific polygraph 1500 in accordance with some embodiments of thepresent disclosure. The example depicted in FIG. 15B illustrates anembodiment in which an alert 1526 is presented in the polygraph 1500. Inthis particular example, the alert 1526 is generated for a messagingapplication 1512 that is connected to an unknown network 1524. Asillustrated in the alert 1526, a malicious IP address has beenidentified in the unknown network 1524. Although not illustrated in thisexample, the alert 1526 may be coupled with functionality that isaccessible in the displayed 1526 alert, which such functionality caninclude ignoring the alert, terminating the application, initiating aremediation workflow, or taking some other action.

Readers will appreciate that the polygraph 1500 depicted in FIGS. 15Aand 15B is just one example of a user-specific polygraph that may begenerated and utilized as described above. In other embodiments, less oradditional information may be included in the polygraph 1500, differentinformation may be included in the polygraph 1500, different actions maybe initiated via the polygraph 1500, or the polygraph 1500 may otherwisediffer from the depicted examples.

One or more embodiments may be described herein with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

While particular combinations of various functions and features of theone or more embodiments are expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method of detecting anomalous behavior of adevice, the method comprising: generating, using information describinghistorical activity associated with a device associated with a user, atrained model for detecting normal activity for the device, wherein thetrained model is specific to the device; gathering informationdescribing current activity associated with the device; determining, byusing the information describing current activity associated with thedevice as input to the trained model, whether the device has deviatedfrom normal activity; and initiating a remediation workflow afterdetermining that device has deviated from normal activity.
 2. The methodof claim 1 wherein: generating a trained model for detecting normalactivity for the device further comprises generating the trained modelusing information describing locations at which the device was utilized;and gathering information describing current activity associated withthe device further comprises gathering information describing a locationat which the device is currently being used.
 3. The method of claim 1wherein: generating a trained model for detecting normal activity forthe device further comprises generating the trained model usinginformation describing usage of one or more applications accessed by thedevice; and gathering information describing current activity associatedwith the device further comprises gathering information describing usageof applications accessed by the device.
 4. The method of claim 1wherein: generating a trained model for detecting normal activity forthe device further comprises generating the trained model usinginformation describing times at which activity previously occurred; andgathering information describing current activity associated with thedevice further comprises gathering information describing times at whichcurrent activity occurred on the device.
 5. The method of claim 1further comprising periodically retraining the trained model.
 6. Themethod of claim 1 further comprising generating an alert afterdetermining that the device has deviated from normal activity.
 7. Themethod of claim 1, wherein the remediation workflow is configured toprevent abnormal activity by the device.
 8. The method of claim 1,wherein the remediation workflow is configured to enable abnormalactivity by the device upon satisfaction of a condition.
 9. The methodof claim 1, wherein the condition comprises a third-party approval or auser authentication.
 10. A system for detecting anomalous behavior of adevice, the system including at least one processor and memory storingcomputer program instructions that, when executed, cause the system tocarry out the steps of: generating, using information describinghistorical activity associated with a device associated with a user, atrained model for detecting normal activity for the device, wherein thetrained model is specific to the device; gathering informationdescribing current activity associated with the device; determining, byusing the information describing current activity associated with thedevice as input to the trained model, whether the device has deviatedfrom normal activity; and initiating a remediation workflow afterdetermining that device has deviated from normal activity.
 11. Thesystem of claim 10 wherein: generating a trained model for detectingnormal activity for the device further comprises generating the trainedmodel using information describing physical locations at which thedevice was utilized; and gathering information describing currentactivity associated with the device further comprises gatheringinformation describing a location at which the device is currently beingused.
 12. The system of claim 10 wherein: generating a trained model fordetecting normal activity for the device further comprises generatingthe trained model using information describing usage of one or moreapplications accessed by the device; and gathering informationdescribing current activity associated with the device further comprisesgathering information describing usage of applications accessed by thedevice.
 13. The system of claim 10 wherein: generating a trained modelfor detecting normal activity for the device further comprisesgenerating the trained model using information describing times at whichactivity previously occurred; and gathering information describingcurrent activity associated with the device further comprises gatheringinformation describing times at which current activity occurred on thedevice.
 14. The system of claim 10 further comprising computer programinstructions that, when executed, cause the system to carry out the stepof periodically retraining the trained model.
 15. The system of claim 10further comprising computer program instructions that, when executed,cause the system to carry out the step of generating an alert afterdetermining that the device has deviated from normal activity.
 16. Acomputer program product for detecting anomalous behavior of a device,the computer program product disposed on a non-transitory computerreadable medium, the computer program product including computer programinstructions that, when executed, carry out the steps of: generating,using information describing historical activity associated with adevice associated with a user, a trained model for detecting normalactivity for the device, wherein the trained model is specific to thedevice; gathering information describing current activity associatedwith the device; determining, by using the information describingcurrent activity associated with the device as input to the trainedmodel, whether the device has deviated from normal activity; andinitiating a remediation workflow after determining that device hasdeviated from normal activity.
 17. The computer program product of claim16 wherein: generating a trained model for detecting normal activity forthe device further comprises generating the trained model usinginformation describing locations at which the device was utilized; andgathering information describing current activity associated with thedevice further comprises gathering information describing a location atwhich the device is currently being used.
 18. The computer programproduct of claim 16 wherein: generating a trained model for detectingnormal activity for the device further comprises generating the trainedmodel using information describing usage of one or more applicationsaccessed by the device; and gathering information describing currentactivity associated with the device further comprises gatheringinformation describing usage of applications accessed by the device. 19.The computer program product of claim 16 wherein: generating a trainedmodel for detecting normal activity for the device further comprisesgenerating the trained model using information describing times at whichactivity previously occurred; and gathering information describingcurrent activity associated with the device further comprises gatheringinformation describing times at which current activity occurred on thedevice.
 20. The computer program product of claim 16 further comprisingcomputer program instructions that, when executed, carry out the step ofperiodically retraining the trained model.